ppt about short text large effect measuring the impact of user reviews on android app security & privacy
this is for presentation NLP study in Hanyang-University
If Anyone wants, You may use this
I summarized that I've researched super-resolution tasks. In detail, I studied the SR as unsupervised learning which doesn't need some ground-truth high-resolution dataset. But I changed the main topic from unsupervised learning. That is SR is based on continual learning.
I'm continually researching the unsupervised SR that I cannot finish in the past. Thus, it's an honor any contact which is interesting in the SR.
(You can refer to these slides anytime.)
[CVPRW2021]FReTAL: Generalizing Deepfake detection using Knowledge Distillati...KIMMINHA3
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This is my first paper when I was a graduate student.
I really appreciate it if you want to use this presentation, and I would if it is useful for you. If you use it, please refer to my name or my ID for your future presentation.
We propose a novel domain adaption framework, โFeature Representation Transfer Adaptation Learningโ (FReTAL), based on knowledge distillation and representation learning that can prevent catastrophic forgetting without accessing the source domain data.
We show that leveraging knowledge distillation and representation learning can enhance adaptability across different deepfake domains.
We demonstrate that our method outperforms baseline approaches on deepfake benchmark datasets with up to 86.97% accuracy on low-quality deepfake detection.
We propose a novel domain adaption framework, โFeature Representation Transfer Adaptation Learningโ (FReTAL), based on knowledge distillation and representation learning that can prevent catastrophic forgetting without accessing to the source domain data.
We show that leveraging knowledge distillation and representation learning can enhance adaptability across different deepfake domains.
We demonstrate that our method outperforms baseline approaches on deepfake benchmark datasets with up to 86.97% accuracy on low-quality deepfake detection.
They proposed two novel methods.
1. Stripe-Wise Pruning (SWP)
They propose a new pruning paradigm called SWP (Stripe-Wise Pruning)
They achieve a higher pruning ratio compared to the filter-wise, channel-wise, and group-wise pruning methods.
2. Filter Skeleton (FS)
They propose a new method โFilter Skeletonโ to efficiently learn the optimal shape of the filters for pruning.
They didn't much compare with other baselines. But they obviously suggested the novel methods, that is why I choose for review when reviewing the paper. More, they said that It is State-of-the-art (SOTA) method of lately pruning methods.
Methods for interpreting and understanding deep neural networksKIMMINHA3
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This paper "methods for interpreting and understanding deep neural networks" was presented in ICASSP 2017, and introduced by G Montavon et al.
Now, the number of citations is more than 1,370. That is, this paper has a lot of things to study for deep learning technology.
Meta learned Confidence for Few-shot LearningKIMMINHA3
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This was presented Meta learned Confidence for Few-shot Learning on CVPR in 2020.
Few-shot learning is an important challenge under data scarcity.
When there is a lot of unlabeled data and data scarcity,
a) leveraging nearest neighbor graph
b) using predicted soft or hard labels on unlabeled samples to update the class prototype.
the model confidence may be unreliable, which may lead to incorrect predictions.
[CVPRW 2020]Real world Super-Resolution via Kernel Estimation and Noise Injec...KIMMINHA3
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This paper is about the Super-Resolution (SR) task and was introduced in CVPRW 2020 as the winner of two tasks with SR competition.
The authors called into question why there are no practical methods for denoising. Because previous papers dealt with ideal noise like bicubic downsampling.
To solve this impractical and ideal problem, the authors proposed to improve the resolution via kernel estimation and noise injection, which means that they do not use it while the training phase. That is why I was interested in this paper.
It is simply for before training. So I was interested in how they explore the proper with real-world images; kernel estimation and noise injection.
In summary, they save some informs of kernels that are applied corresponding to their formula using the eval data, i.e., no have ground truth. Also, the values of noise are as well.
These are what they are emphasizing novel method.
If you guys want to see and know more specifically this paper, you can cite this link:
https://openaccess.thecvf.com/content_CVPRW_2020/papers/w31/Ji_Real-World_Super-Resolution_via_Kernel_Estimation_and_Noise_Injection_CVPRW_2020_paper.pdf
In this presentation, I'll introduce the 'Real-world Super-Resolution via Kernel Estimation and Noise Injection'
To present on the seminar in DASH-Lab, SKKU, I brought out the thesis, which is Transferable GAN-generated Images (ICML 2020)
Detection.
.
If you want to see the context more specifically, you can see from this link : https://arxiv.org/abs/2008.04115
short text large effect measuring the impact of user reviews on android app security & privacy
1. Short Text, Large Effect:
Measuring the Impact of
User Reviews on Android
App Security & Privacy
Duc Cuong Nguyenโ, Erik Derrโ, Michael Backesโ , Sven Bugielโ
โCISPA, Saarland University โ CISPA Helmholtz Center i.G.
์ ์๊ณตํ๋ถ ๊น๋ฏผํ