對抗樣本(論文解讀十二): Imagenet-trained cnns are Biased towards Texture; Increasing Shape Bias Improves
Imagenet-trained cnns are Biased towards Texture; Increasing Shape Bias Improves Accuracy And Robustness RobertGeirhos University of T¨ubingen & IMPRS-IS [email protected] PatriciaRubisch University o
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- 1. IMAGENET-TRAINED CNNS ARE BIASED TOWARDS TEXTURE; INCREASING SHAPE BIAS IMPROVES ACCURACY AND ROB...
- 2. 對抗樣本(論文解讀十一):PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning
- 3. 對抗樣本(論文解讀八):Towards More Robust Adversarial Attack Against Real World Object Detectors
- 4. 對抗樣本(論文解讀三): Adversarial Examples Improve Image Recognition
- 5. 對抗樣本(論文解讀五):Perceptual-Sensitive GAN for Generating Adversarial Patches
- 6. 對抗樣本(論文解讀七):On Physical Adversarial Patches for Object Detection
- 7. 對抗樣本論文學習：Deep Neural Networks are Easily Fooled
- 8. INQ 論文解讀：Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights
- 9. ECCV 2020 的對抗相關論文（對抗生成、對抗攻擊）
- 10. Gated-SCNN: Gated Shape CNNs for Semantic Segmentation——論文閱讀理解
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相關文章
- 1. IMAGENET-TRAINED CNNS ARE BIASED TOWARDS TEXTURE; INCREASING SHAPE BIAS IMPROVES ACCURACY AND ROB...
- 2. 對抗樣本(論文解讀十一):PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning
- 3. 對抗樣本(論文解讀八):Towards More Robust Adversarial Attack Against Real World Object Detectors
- 4. 對抗樣本(論文解讀三): Adversarial Examples Improve Image Recognition
- 5. 對抗樣本(論文解讀五):Perceptual-Sensitive GAN for Generating Adversarial Patches
- 6. 對抗樣本(論文解讀七):On Physical Adversarial Patches for Object Detection
- 7. 對抗樣本論文學習：Deep Neural Networks are Easily Fooled
- 8. INQ 論文解讀：Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights
- 9. ECCV 2020 的對抗相關論文（對抗生成、對抗攻擊）
- 10. Gated-SCNN: Gated Shape CNNs for Semantic Segmentation——論文閱讀理解