AAAI2023「Are Transformers Effective for Time Series Forecasting?」と、HuggingFace「Yes, Transformers are Effective for Time Series Forecasting (+ Autoformer)」の紹介です。
AAAI2023「Are Transformers Effective for Time Series Forecasting?」と、HuggingFace「Yes, Transformers are Effective for Time Series Forecasting (+ Autoformer)」の紹介です。
First part shows several methods to sample points from arbitrary distributions. Second part shows application to population genetics to infer population size and divergence time using obtained sequence data.
1. Two papers on unsupervised domain adaptation were presented at ICML2018: "Learning Semantic Representations for Unsupervised Domain Adaptation" and "CyCADA: Cycle-Consistent Adversarial Domain Adaptation".
2. The CyCADA paper uses cycle-consistent adversarial domain adaptation with cycle GAN to translate images at the pixel level while also aligning representations at the semantic level.
3. The semantic representation paper uses semantic alignment and introduces techniques like adding noise to improve over previous semantic alignment methods.
You Only Look One-level Featureの解説と見せかけた物体検出のよもやま話Yusuke Uchida
第7回全日本コンピュータビジョン勉強会「CVPR2021読み会」(前編)の発表資料です
https://kantocv.connpass.com/event/216701/
You Only Look One-level Featureの解説と、YOLO系の雑談や、物体検出における関連する手法等を広く説明しています
First part shows several methods to sample points from arbitrary distributions. Second part shows application to population genetics to infer population size and divergence time using obtained sequence data.
1. Two papers on unsupervised domain adaptation were presented at ICML2018: "Learning Semantic Representations for Unsupervised Domain Adaptation" and "CyCADA: Cycle-Consistent Adversarial Domain Adaptation".
2. The CyCADA paper uses cycle-consistent adversarial domain adaptation with cycle GAN to translate images at the pixel level while also aligning representations at the semantic level.
3. The semantic representation paper uses semantic alignment and introduces techniques like adding noise to improve over previous semantic alignment methods.
You Only Look One-level Featureの解説と見せかけた物体検出のよもやま話Yusuke Uchida
第7回全日本コンピュータビジョン勉強会「CVPR2021読み会」(前編)の発表資料です
https://kantocv.connpass.com/event/216701/
You Only Look One-level Featureの解説と、YOLO系の雑談や、物体検出における関連する手法等を広く説明しています
【論文紹介】Spatial Temporal Graph Convolutional Networks for Skeleton-Based Acti...ddnpaa
(参考文献)Sijie Yan, Yuanjun Xiong, Dahua Lin.Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition. Association for the Advancement of Artificial Intelligence (AAAI)2018
[2010]
Large-scale Image Classification: Fast Feature Extraction and SVM Training
[2011]
High-dimensional signature compression for large-scale image classification
16. MnasNet: Platform-Aware Neural Architecture Search for Mobile
• Mingxing Tan et al., Google Brain
• arXiv:1807.11626v1
16
紹介する論文
• 性能だけでなく,modelの処理速度も考慮した多目的構造最適化
手法
• mobile phoneでの実行速度を最適化に使用
I will talk about image restoration using evolutionary search.
Image restoration is to recover a clean image from its corrupted version.
These are image restoration tasks, image inpainting and denoising.
In order to solve this task, learning-based methods which use CNNs have been introduced, and have shown good performance.
In these studies, researchers have approached the problem mainly from two directions.
One is to design new network architectures.
For example, the network of the bottom left is called MemNet, which contains many recursive connections and gate units.
The other is to develop new loss functions or training methods.
A recent trend is to use adversarial training, in which a generator is trained to perform image restoration, and a discriminator is trained to distinguish whether an input image is true image or a recovered one.