Abstract: While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification have been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.
Despite their strong transfer performance, deep convolutional representations surprisingly lack a basic low-level property -- shift-invariance, as small input shifts or translations can cause drastic changes in the output. Commonly used downsampling methods, such as max-pooling, strided-convolution, and average-pooling, ignore the sampling theorem. The well-known signal processing fix is anti-aliasing by low-pass filtering before downsampling. However, simply inserting this module into deep networks degrades performance; as a result, it is seldomly used today. We show that when integrated correctly, it is compatible with existing architectural components, such as max-pooling and strided-convolution. We observe increased accuracy in ImageNet classification, across several commonly-used architectures, such as ResNet, DenseNet, and MobileNet, indicating effective regularization. Furthermore, we observe better generalization, in terms of stability and robustness to input corruptions. Our results demonstrate that this classical signal processing technique has been undeservingly overlooked in modern deep networks.
Depth estimation do we need to throw old things awayNAVER Engineering
발표의 개요 : Human visual system 기반의 CNN for depth estimation과 CNN inspired by conventional methods
Case1: Cross-channel stereo matching
Case2: Depth from light field
Case3: Multiview stereo
Conclusion
Synthesizing pseudo 2.5 d content from monocular videos for mixed realityNAVER Engineering
Free-viewpoint video (FVV) is a kind of advanced media that provides a more immersive user experience than traditional media. It allows users to interact with content because users can view media at the desired viewpoint and is becoming a next-generation media.
In creating FVV content, existing systems require complex and specialized capturing equipment and has low end-user usability because it needs a lot of expertise to use the system. This becomes an inconvenience for individuals or small organizations who want to create content and limits the end user’s ability to create FVV-based user-generated content (UGC) and inhibits the creation and sharing of various created content.
To tackle these problems, ParaPara is proposed in this work. ParaPara is an end-to-end system that uses a simple yet effective method to generate pseudo-2.5D FVV content from monocular videos, unlike the previously proposed systems. First, the system detects persons from the monocular video through a deep neural network, calculates the real-world homography matrix based on the minimal user interaction, and estimates the pseudo-3D positions of the detected persons. Then, person textures are extracted using general image processing algorithms and placed at the estimated real-world positions. Finally, the pseudo-2.5D content is synthesized from these elements. The content, which is synthesized by the proposed system, is implemented on Microsoft HoloLens; the user can freely place the generated content on the real world and watch it on a free viewpoint.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/person-re-identification-and-tracking-at-the-edge-challenges-and-techniques-a-presentation-from-the-university-of-auckland/
Morteza Biglari-Abhari, Senior Lecturer at the University of Auckland, presents the “Person Re-Identification and Tracking at the Edge: Challenges and Techniques” tutorial at the May 2021 Embedded Vision Summit.
Numerous video analytics applications require understanding how people are moving through a space, including the ability to recognize when the same person has moved outside of the camera’s view and then back into the camera’s view, or when a person has passed from the view of one camera to the view of another. This capability is referred to as person re-identification and tracking. It’s an essential technique for applications such as surveillance for security, health and safety monitoring in healthcare and industrial facilities, intelligent transportation systems and smart cities. It can also assist in gathering business intelligence such as monitoring customer behavior in shopping environments. Person re-identification is challenging.
In this talk, Biglari-Abhari discusses the key challenges and current approaches for person re-identification and tracking, as well as his initial work on multi-camera systems and techniques to improve accuracy, especially fusing appearance and spatio-temporal models. He also briefly discusses privacy-preserving techniques, which are critical for some applications, as well as challenges for real-time processing at the edge.
Depth estimation do we need to throw old things awayNAVER Engineering
발표의 개요 : Human visual system 기반의 CNN for depth estimation과 CNN inspired by conventional methods
Case1: Cross-channel stereo matching
Case2: Depth from light field
Case3: Multiview stereo
Conclusion
Synthesizing pseudo 2.5 d content from monocular videos for mixed realityNAVER Engineering
Free-viewpoint video (FVV) is a kind of advanced media that provides a more immersive user experience than traditional media. It allows users to interact with content because users can view media at the desired viewpoint and is becoming a next-generation media.
In creating FVV content, existing systems require complex and specialized capturing equipment and has low end-user usability because it needs a lot of expertise to use the system. This becomes an inconvenience for individuals or small organizations who want to create content and limits the end user’s ability to create FVV-based user-generated content (UGC) and inhibits the creation and sharing of various created content.
To tackle these problems, ParaPara is proposed in this work. ParaPara is an end-to-end system that uses a simple yet effective method to generate pseudo-2.5D FVV content from monocular videos, unlike the previously proposed systems. First, the system detects persons from the monocular video through a deep neural network, calculates the real-world homography matrix based on the minimal user interaction, and estimates the pseudo-3D positions of the detected persons. Then, person textures are extracted using general image processing algorithms and placed at the estimated real-world positions. Finally, the pseudo-2.5D content is synthesized from these elements. The content, which is synthesized by the proposed system, is implemented on Microsoft HoloLens; the user can freely place the generated content on the real world and watch it on a free viewpoint.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/person-re-identification-and-tracking-at-the-edge-challenges-and-techniques-a-presentation-from-the-university-of-auckland/
Morteza Biglari-Abhari, Senior Lecturer at the University of Auckland, presents the “Person Re-Identification and Tracking at the Edge: Challenges and Techniques” tutorial at the May 2021 Embedded Vision Summit.
Numerous video analytics applications require understanding how people are moving through a space, including the ability to recognize when the same person has moved outside of the camera’s view and then back into the camera’s view, or when a person has passed from the view of one camera to the view of another. This capability is referred to as person re-identification and tracking. It’s an essential technique for applications such as surveillance for security, health and safety monitoring in healthcare and industrial facilities, intelligent transportation systems and smart cities. It can also assist in gathering business intelligence such as monitoring customer behavior in shopping environments. Person re-identification is challenging.
In this talk, Biglari-Abhari discusses the key challenges and current approaches for person re-identification and tracking, as well as his initial work on multi-camera systems and techniques to improve accuracy, especially fusing appearance and spatio-temporal models. He also briefly discusses privacy-preserving techniques, which are critical for some applications, as well as challenges for real-time processing at the edge.
Learning Disentangled Representation for Robust Person Re-identificationNAVER Engineering
We address the problem of person re-identification (reID), that is, retrieving person images from a large dataset, given a query image of the person of interest. The key challenge is to learn person representations robust to intra-class variations, as different persons can have the same attribute and the same person's appearance looks different with viewpoint changes. Recent reID methods focus on learning discriminative features but robust to only a particular factor of variations (e.g., human pose) and this requires corresponding supervisory signals (e.g., pose annotations). To tackle this problem, we propose to disentangle identity-related and -unrelated features from person images. Identity-related features contain information useful for specifying a particular person (e.g.,clothing), while identity-unrelated ones hold other factors (e.g., human pose, scale changes). To this end, we introduce a new generative adversarial network, dubbed identity shuffle GAN (IS-GAN), that factorizes these features using identification labels without any auxiliary information. We also propose an identity shuffling technique to regularize the disentangled features. Experimental results demonstrate the effectiveness of IS-GAN, largely outperforming the state of the art on standard reID benchmarks including the Market-1501, CUHK03 and DukeMTMC-reID. Our code and models will be available online at the time of the publication.
To get this project in ONLINE or through TRAINING Sessions,
Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: jpinfotechprojects@gmail.com, web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com
Literature Review on Single Image Super Resolutionijtsrd
In this paper, a detailed survey study on single image super-resolution (SR) has been presented, which aims at recovering a high-resolution (HR) image from a given low-resolution (LR) one. It is always the research emphasis because of the requirement of higher definition video displaying, such as the new generation of Ultra High Definition (UHD) TVs. Super-resolution (SR) is a popular topic of image processing that focuses on the enhancement of image resolution. In general, SR takes one or several low-resolution (LR) images as input and maps them as output images with high resolution (HR), which has been widely applied in remote sensing, medical imaging, biometric identification. Shalini Dubey | Prof. Pankaj Sahu | Prof. Surya Bazal"Literature Review on Single Image Super Resolution" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18339.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/18339/literature-review-on-single-image-super-resolution/shalini-dubey
AI+ Remote Sensing: Applying Deep Learning to Image Enhancement, Analytics, a...Jui-Hsin (Larry) Lai
A Keynote Speech in DigitalWorld Congress 2021.
Youtube Video https://www.youtube.com/watch?v=FdUyaUJTYLY
In this talk, we introduce our proposed AI+ Remote Sensing techniques from the Research Lab of Ping An Technology. One of the techniques is our deep learning haze removal model which can effectively remove the interference of haze in the satellite images and observe the true ground reflectance. Next, we introduce our super-resolution model which can enhance 4x image details. The SR model has been deployed to the Sentinel-2 satellite imagery and greatly improve its image quality. Last, we introduce our crop recognition system. The system includes a user interface for a user to label a few of training samples, and the proposed crop recognition model can be trained on the fly to be deployed on a broad geo-area immediately. In addition to the techniques, our AI+ Remote Sensing technologies have been supporting the carbon(CO2) emission analysis for Environment, Society, and Government(ESG) Department, flooding and disaster analysis for Smart City Department, and crop field forecast for Investment Department in Ping An Group.
In this talk, we introduce our proposed AI+ Remote Sensing techniques from the Research Lab of Ping An Technology. One of the techniques is our deep learning haze removal model which can effectively remove the interference of haze in the satellite images and observe the true ground reflectance. Next, we introduce our super-resolution model which can enhance 4x image details. The SR model has been deployed to the Sentinel-2 satellite imagery and greatly improve its image quality. Last, we introduce our crop recognition system. The system includes a user interface for a user to label a few of training samples, and the proposed crop recognition model can be trained on the fly to be deployed on a broad geo-area immediately. In addition to the techniques, our AI+ Remote Sensing technologies have been supporting the carbon(CO2) emission analysis for Environment, Society, and Government(ESG) Department, flooding and disaster analysis for Smart City Department, and crop field forecast for Investment Department in Ping An Group.
http://ixa2.si.ehu.es/deep_learning_seminar/
Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language and vision. Image captioning, visual question answering or multimodal translation are some of the first applications of a new and exciting field that exploiting the generalization properties of deep neural representations. This talk will provide an overview of how vision and language problems are addressed with deep neural networks, and the exciting challenges being addressed nowadays by the research community.
2019년 파이콘 한국에서 진행된 튜토리얼 자료입니다. 최재식 교수님께서 설명가능인공지능이란 무엇인가에 대해 발표해주신 Part 1 발표자료입니다. 아래 링크를 통해 행사 관련 정보를 확인하실 수 있습니다.
http://xai.unist.ac.kr/Tutorial/2018/
https://github.com/OpenXAIProject/PyConKorea2019-Tutorials
Part 1: https://www.slideshare.net/OpenXAI/2019-part-1
Part 2: https://www.slideshare.net/OpenXAI/2019-lrp-part-2
Part 3: https://www.slideshare.net/OpenXAI/2019-shap-part-3
Learning Disentangled Representation for Robust Person Re-identificationNAVER Engineering
We address the problem of person re-identification (reID), that is, retrieving person images from a large dataset, given a query image of the person of interest. The key challenge is to learn person representations robust to intra-class variations, as different persons can have the same attribute and the same person's appearance looks different with viewpoint changes. Recent reID methods focus on learning discriminative features but robust to only a particular factor of variations (e.g., human pose) and this requires corresponding supervisory signals (e.g., pose annotations). To tackle this problem, we propose to disentangle identity-related and -unrelated features from person images. Identity-related features contain information useful for specifying a particular person (e.g.,clothing), while identity-unrelated ones hold other factors (e.g., human pose, scale changes). To this end, we introduce a new generative adversarial network, dubbed identity shuffle GAN (IS-GAN), that factorizes these features using identification labels without any auxiliary information. We also propose an identity shuffling technique to regularize the disentangled features. Experimental results demonstrate the effectiveness of IS-GAN, largely outperforming the state of the art on standard reID benchmarks including the Market-1501, CUHK03 and DukeMTMC-reID. Our code and models will be available online at the time of the publication.
To get this project in ONLINE or through TRAINING Sessions,
Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: jpinfotechprojects@gmail.com, web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com
Literature Review on Single Image Super Resolutionijtsrd
In this paper, a detailed survey study on single image super-resolution (SR) has been presented, which aims at recovering a high-resolution (HR) image from a given low-resolution (LR) one. It is always the research emphasis because of the requirement of higher definition video displaying, such as the new generation of Ultra High Definition (UHD) TVs. Super-resolution (SR) is a popular topic of image processing that focuses on the enhancement of image resolution. In general, SR takes one or several low-resolution (LR) images as input and maps them as output images with high resolution (HR), which has been widely applied in remote sensing, medical imaging, biometric identification. Shalini Dubey | Prof. Pankaj Sahu | Prof. Surya Bazal"Literature Review on Single Image Super Resolution" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18339.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/18339/literature-review-on-single-image-super-resolution/shalini-dubey
AI+ Remote Sensing: Applying Deep Learning to Image Enhancement, Analytics, a...Jui-Hsin (Larry) Lai
A Keynote Speech in DigitalWorld Congress 2021.
Youtube Video https://www.youtube.com/watch?v=FdUyaUJTYLY
In this talk, we introduce our proposed AI+ Remote Sensing techniques from the Research Lab of Ping An Technology. One of the techniques is our deep learning haze removal model which can effectively remove the interference of haze in the satellite images and observe the true ground reflectance. Next, we introduce our super-resolution model which can enhance 4x image details. The SR model has been deployed to the Sentinel-2 satellite imagery and greatly improve its image quality. Last, we introduce our crop recognition system. The system includes a user interface for a user to label a few of training samples, and the proposed crop recognition model can be trained on the fly to be deployed on a broad geo-area immediately. In addition to the techniques, our AI+ Remote Sensing technologies have been supporting the carbon(CO2) emission analysis for Environment, Society, and Government(ESG) Department, flooding and disaster analysis for Smart City Department, and crop field forecast for Investment Department in Ping An Group.
In this talk, we introduce our proposed AI+ Remote Sensing techniques from the Research Lab of Ping An Technology. One of the techniques is our deep learning haze removal model which can effectively remove the interference of haze in the satellite images and observe the true ground reflectance. Next, we introduce our super-resolution model which can enhance 4x image details. The SR model has been deployed to the Sentinel-2 satellite imagery and greatly improve its image quality. Last, we introduce our crop recognition system. The system includes a user interface for a user to label a few of training samples, and the proposed crop recognition model can be trained on the fly to be deployed on a broad geo-area immediately. In addition to the techniques, our AI+ Remote Sensing technologies have been supporting the carbon(CO2) emission analysis for Environment, Society, and Government(ESG) Department, flooding and disaster analysis for Smart City Department, and crop field forecast for Investment Department in Ping An Group.
http://ixa2.si.ehu.es/deep_learning_seminar/
Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language and vision. Image captioning, visual question answering or multimodal translation are some of the first applications of a new and exciting field that exploiting the generalization properties of deep neural representations. This talk will provide an overview of how vision and language problems are addressed with deep neural networks, and the exciting challenges being addressed nowadays by the research community.
2019년 파이콘 한국에서 진행된 튜토리얼 자료입니다. 최재식 교수님께서 설명가능인공지능이란 무엇인가에 대해 발표해주신 Part 1 발표자료입니다. 아래 링크를 통해 행사 관련 정보를 확인하실 수 있습니다.
http://xai.unist.ac.kr/Tutorial/2018/
https://github.com/OpenXAIProject/PyConKorea2019-Tutorials
Part 1: https://www.slideshare.net/OpenXAI/2019-part-1
Part 2: https://www.slideshare.net/OpenXAI/2019-lrp-part-2
Part 3: https://www.slideshare.net/OpenXAI/2019-shap-part-3
Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language, vision and speech. Image captioning, lip reading or video sonorization are some of the first applications of a new and exciting field of research exploiting the generalization properties of deep learning. In this talk we will review the latest results on how convolutional and recurrent neural networks are combined to find the most hidden patterns in multimedia.
https://telecombcn-dl.github.io/2018-dlmm/
achine Learning and deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. Ever wondered what all the fuss is about? Or what these technologies can do for you? Are you working in the field and wish to enhance your current knowledge in some specific techniques?
Insight@DCU will host a 2 day workshop on Machine Learning on May 21st and 22nd, which will help to answer your questions, whether a novice or knowledgeable in the field.
This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
TOP 5 Most View Article From Academia in 2019sipij
TOP 5 Most View Article From Academia in 2019
Signal & Image Processing : An International Journal (SIPIJ)
ISSN : 0976 - 710X (Online) ; 2229 - 3922 (print)
http://www.airccse.org/journal/sipij/index.html
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
Details of Lazy Deep Learning for Images Recognition in ZZ Photo appPAY2 YOU
В докладе представлена тема глубокого обучения (Deep Learning) для распознавания изображений. Рассматриваются практические аспекты обучения глубоких сверточных сетей на GPU, обсуждается личный опыт портирования обученных нейросетей в приложение на основе библиотеки OpenCV, проводится сравнение полученного детектора домашних животных на основе подхода Lazy Deep Learning с детектором Виолы-Джонса.
Докладчики: Артем Чернодуб – эксперт в области искусственных нейронных сетей и систем искусственного интеллекта. В 2007 году закончил Московский физико-технический институт. Руководит направлением Computer Vision в компании ZZ Wolf, а также по совместительству работает научным сотрудником в Институте проблем математических машин и систем НАНУ.
Юрий Пащенко – специалист в области систем машинного зрения и машинного обучения, магистр НТУУ «Киевский Политехнический Институт», факультет прикладной математики (2014). Работает в компании ZZ Wolf на должности R&D Engineer.
Computer Vision Fundamentals
Human vision and perception
Comparision of computer vision to human vision
Cognition
SIFT Algorithm teardown
Computer Vision Grand Challenges
https://telecombcn-dl.github.io/dlmm-2017-dcu/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
비행기 설계를 왜 통일 해야 할까?
디자인 시스템을 하는 이유
비행기들이 다 용도가 다르다...어떻게 설계하지?
맥락이 다른 페이지와 패턴
경유지까지 아직 멀었다... 언제 수리하지?
디자인 시스템을 적용하는 시점
엔지니어랑 얘기해서 정비해야하는데...어떻게 수리하지?
디자인 시스템을 적용하는 프로세스
비행기 설계가 바뀐걸 어떻게 알리지?
디자인 시스템의 전파
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
20. Egomotion
Agrawal et al. ICCV 2015. Jayaraman et al. ICCV 2015.
Context
Noroozi and Favaro. ECCV 2016.
Doersch et al. ICCV 2015.
Pathak et al. CVPR 2016.
Hinton & Salakhutdinov.
Science 2006.
Wang et al. ICCV 2015. Pathak et al. CVPR 2017.
Misra et al. ECCV 2016.
de Sa. NIPS 1994.
Video
Audio
Autoencoders Denoising Autoencoders
Vincent et al. ICML 2008.
Goal: Set up a pre-training scheme to induce a “useful” representation
Owens et al. ECCV 2016.
Arandjelovic & Zisserman. ICCV 17
Generative Modeling
Donahue et al. Dumoulin et al. ICLR 2017.
21
23. 24
X
Raw Data
X1
X2
Induce abstraction through prediction
Cross-Channel Encoder
c.f. Larsson et al. Colorization as a Proxy Task for Visual Understanding. In CVPR, 2017.
24. 25
Zhang, Isola, Efros. Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction. In CVPR, 2017.
c.f. LeCun, Denker, Solla. Optimal Brain Damage. In NIPS, 1989.
X
X1
X2
X1
X2
X
Split-Brain Autoencoder
25. Input Image X Predicted Image X
26
Split-Brain Autoencoder on Images
29. 31
Task & Dataset Generalization
Does the feature representation transfer
to other tasks and datasets?
Berkeley-Adobe Perceptual
Patch Similarity Dataset
Zhang, Isola, Efros, Shechtman, Wang. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. In CVPR, 2018.
D ( , )
Rock beauty
Classification Perceptual Judgments
30. 32
Zhang, Isola, Efros, Shechtman, Wang. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. In CVPR, 2018.
How different are these patches?
31. Which patch is more similar to the middle?
Humans
L2/PSNR
SSIM/FSIMc
Deep Networks?
< Clap >< Clap >
33
32. “Perceptual Losses”
34
Gatys et al. In CVPR, 2016.
Johnson et al. In ECCV, 2016.
Dosovitskiy and Brox. In NIPS, 2016.
Chen and Koltun. In ICCV, 2017.
33. Deep Networks as a Perceptual Metric
𝐹 𝐹
Normalize,
Subtract
L2 norm,
Spatial average
𝑥 𝑥0
Avg
𝑑0
(1) How well do “perceptual losses” describe perception?
(2) Does it have to be VGG trained on classification?
c.f. Gatys et al. CVPR 2016. Johnson et al. ECCV 2016. Dosovitskiy and Brox. NIPS 2016.
35
37. 40
% agreement with
human judges
Bigger/Deeper ≠ Better
Networks perform strongly across supervisory
signals and architectures
Fitting some data
is important
82.6
68.9
Low-level
AlexNet (Random)
AlexNet (Unsupervised)
AlexNet (Self-supervised)
Nets (Supervised -
Imagenet classification)
Human
75.7
76.8
78.0
76.876.4
75.5
74.8
70.6
70.069.7
VGG on classification
(“perceptual loss”) correlates well
Can we train on perceptual judgments?
40. Training a Perceptual Metric
𝐹 𝐹
Normalize,
Subtract
L2 norm,
Spatial average
𝑥 𝑥0
Avg
𝑑0
c.f. Gatys et al. CVPR 2016. Johnson et al. ECCV 2016. Dosovitskiy and Brox. NIPS 2016.
𝑤
Off-the-shelf (w=1)
Frozen (w learned)
Tuned (w learned)
41. Off-the-shelf networks already perform well
Training a linear layer on top yields small performance boost.
Fine-tuning through representation leads to overfitting
Training distribution
82.6
76.8
80.6
Held-out distribution
69.5
65.0
64.3
Training a Perceptual Metric
Off-the-shelf Tuned Human Off-the-shelf Tuned Human
78.7
Frozen
65.3
Frozen
“LPIPS” metric: richzhang.github.io/PerceptualSimilarity
Additionally
- Ensembled-LPIPS. Kettunen, Härkönen, Lehtinen. ArXiv 2019.
- Audio domain. Manocha, Finkelstein, Jin, Bryan, Zhang, Mysore. In progress.
43. Deep Networks are not Shift-Invariant
P(correct class) P(correct class)
44. Deep Networks are not Shift-Invariant
Azulay and Weiss. Why do deep convolutional networks generalize so poorly to small image transformations? In ArXiv, 2018.
Engstrom, Tsipras, Schmidt, Madry. Exploring the Landscape of Spatial Robustness. In ICML, 2019.
P(correct class) P(correct class)
45. Azulay and Weiss. Why do deep convolutional networks generalize so poorly to small image transformations? In ArXiv, 2018.
Engstrom, Tsipras, Schmidt, Madry. A rotation and a translation suffice: Fooling cnns with simple transformations. In ArXiv,
Deep Networks are not Shift-Invariant
76. Perfect shift-eq.
Large deviation
from shift-eq.
pixels
conv1
pool1
conv2
pool2
conv3
pool3
conv4
pool4
conv5
pool5
classifier
softmax
Shift-equivariance, per layer
Every pooling increases periodicity
77. Alternative downsampling methods
• Blur+subsample
• Antialiasing in signal processing; image processing; graphics
• Max-pooling
• Performs better in deep learning applications [Scherer 2010]
88
78. Alternative downsampling methods
• Blur+subsample
• Antialiasing in signal processing; image processing; graphics
• Max-pooling
• Performs better for deep learning [Scherer 2010]
89
79. Alternative downsampling methods
• Blur+subsample
• Antialiasing in signal processing; image processing; graphics
• Max-pooling
• Performs better for deep learning [Scherer 2010]
90
Reconcile antialiasing with max-pooling
80. Max (densely)
Preserves shift-equivariance
max( )
max( )
Anti-aliased pooling (MaxBlurPool)
Shift-equivariance lost; heavy aliasing
max( )
Strided-MaxPool
max( )
Blur
Preserves shift-eq.
Blur
Equivalent Interpretation
Shift-eq. lost; heavy aliasing
Shift eq. lost, but reduced aliasing
Subsampling
Evaluated together as “BlurPool”
92. Striding aliases(stride=2)
Add antialiasing filter
+ shift-equivariance
+ accuracy
Additionally
+ stability to other perturbations
+ robustness to corruptions
104
Discussion
93. Discriminative Learning
105
“Rock beauty”
Semantic
Labels
Textures? Edges? Parts?
Perceptual similarity?
+ Solve discriminative tasks
+ Learn about the visual world + Force the network to learn about the
visual world for free
Raw Unlabeled
Data
Textures? Edges? Parts?
Perceptual similarity?
Image Synthesis
+ Solve graphics tasks
+ Engineering in inductive biases (shift-invariance) still valuable
94. Computer Vision · Graphics · Deep Learning · Machine Learning
Human Computer Interaction · Natural Language Processing
San Jose San Francisco Seattle
102. Train without Data Augmentation Train with Data Augmentation
Shift-Invariance vs Classification Accuracy
Data augmentation increases both
accuracy and shift-invariance
Engineering in shift-invariance is
“free” data augmentation
Boosts shift-invariance while
maintaining accuracy
103. Our Distortions Real Algorithm Outputs
Human
L2/PSNR
SSIM
FSIMc
Gaussian
K-Means
Watch Obj.
Split-Brain
Puzzle
BiGAN
SqueezeNet
AlexNet
VGG
Low-level
Net (Random)
Net (Unsupervised)
Net (Self-supervised)
Net (Supervised)
Human
Percep-Trained (Frozen)
Percep-Trained (Tuned)
Near human-level performance
within our distortions
Linear “calibration” on our
distortions transfers successfully
Training on direct task is not
the complete solution
115
106. Grayscale Input Larsson et al.
In ECCV, 2016.
Iizuka et al.
In SIGGRAPH, 2016.
Zhang, Isola, Efros.
In ECCV, 2016.
Automatic Results with Deep Networks
118Dorothea Lange. Migrant Mother, 1936.
Library of Congress, Prints & Photographs Division, FSA/OWI Collection, reproduction number: LC-USF34-9058-C
107. 119
Dorothea Lange.
Migrant Mother, 1936.
Library of Congress, Prints & Photographs
Division, FSA/OWI Collection, reproduction
number: LC-USF34-9058-C
Zhang*, Zhu*, Isola, Geng, Lin, Yu, Efros. Real-Time User-Guided Image Colorization with Learned Deep Priors. In SIGGRAPH, 2017.
108. 120
Zhang*, Zhu*, Isola, Geng, Lin, Yu, Efros. Real-Time User-Guided Image Colorization with Learned Deep Priors. In SIGGRAPH, 2017.
Dorothea Lange.
Migrant Mother, 1936.
Library of Congress, Prints & Photographs
Division, FSA/OWI Collection, reproduction
number: LC-USF34-9058-C
109. 121
Zhang*, Zhu*, Isola, Geng, Lin, Yu, Efros. Real-Time User-Guided Image Colorization with Learned Deep Priors. In SIGGRAPH, 2017.
Dorothea Lange.
Migrant Mother, 1936.
Library of Congress, Prints & Photographs
Division, FSA/OWI Collection, reproduction
number: LC-USF34-9058-C