https://github.com/encounter1997/arxiv-daily
My daily arxiv reading note
https://github.com/encounter1997/arxiv-daily
Last synced: 3 months ago
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My daily arxiv reading note
- Host: GitHub
- URL: https://github.com/encounter1997/arxiv-daily
- Owner: encounter1997
- License: mit
- Created: 2021-03-18T08:48:25.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2021-11-10T03:14:41.000Z (over 3 years ago)
- Last Synced: 2025-01-14T18:05:52.642Z (4 months ago)
- Size: 396 KB
- Stars: 30
- Watchers: 6
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Arxiv-Daily
My daily arxiv reading notes.
[2021 March](202103.md)
[2021 April](202104.md)
[2021 June](202106.md)
[2021 July](202107.md)
[2021 Aug](202108.md)
[2021 Sep](202109.md)
## CV (Daily)
#### 20211108
* :star: [EditGAN: High-Precision Semantic Image Editing](https://arxiv.org/pdf/2111.03186.pdf) (NVIDIA, MIT) NIPS
* [Improving Visual Quality of Image Synthesis by A Token-based Generator with Transformers](https://arxiv.org/pdf/2111.03481.pdf) (jianlong fu) NIPS
* 用transformer做图像生成，避免生搬硬套的角度：（1）逐个token的生成有利于保证生成图像的局部特性（2）new perspective: token-based generator
#### 20211110
* [Data Augmentation Can Improve Robustness](https://arxiv.org/pdf/2111.05328.pdf) (NIPS21)
* 对抗训练会面临鲁棒性过拟合问题，本文提出一种数据增强方法来提升鲁棒性
* Adversarial training suffers from robust overfitting, a phenomenon where the robust test accuracy starts to decrease during training. In this paper, we focus on reducing robust overfitting by using common data augmentation schemes.
* We demonstrate that, contrary to previous findings, when combined with model weight averaging, data augmentation can significantly boost robust accuracy.
* [Sliced Recursive Transformer](https://arxiv.org/pdf/2111.05297.pdf) (Eric Xing)
* ICLR submission (65553)
* We present a neat yet effective recursive operation on vision transformers that can improve parameter utilization without involving additional parameters. This is achieved by sharing weights across depth of transformer networks.
* [MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps](https://arxiv.org/pdf/2111.05073.pdf) (NIPS21)
* 提升对抗鲁棒性的最常用方法还是对抗训练，本文提出一种alternative，通过知识蒸馏用鲁棒teacher网络提升student鲁棒性
* First, we theoretically show the transferability of robustness from an adversarially trained teacher model to a student model with the help of mixup augmentation.
* MixACM transfers robustness from a robust teacher to a student by matching activated channel maps generated without expensive adversarial perturbations
* [Self-Interpretable Model with Transformation Equivariant Interpretation]() (NIPS21)
* 解释性网络稳定性很差，容易受到数据扰动或变换的干扰。本文提出一种鲁棒的解释性方法，它在self-interpretable model中引入变换的不变性约束。
* Recent studies have found that interpretation methods can be sensitive and unreliable, where the interpretations can be disturbed by perturbations or transformations of input data.
* To address this issue, we propose to learn robust interpretations through transformation equivariant regularization in a self-interpretable model.