对抗样本(论文解读十二): 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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相关文章
- 1. IMAGENET-TRAINED CNNS ARE BIASED TOWARDS TEXTURE; INCREASING SHAPE BIAS IMPROVES ACCURACY AND ROB...
- 2. 对抗样本(论文解读十一):PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning
- 3. 对抗样本(论文解读八):Towards More Robust Adversarial Attack Against Real World Object Detectors
- 4. 对抗样本(论文解读三): Adversarial Examples Improve Image Recognition
- 5. 对抗样本(论文解读五):Perceptual-Sensitive GAN for Generating Adversarial Patches
- 6. 对抗样本(论文解读七):On Physical Adversarial Patches for Object Detection
- 7. 对抗样本论文学习：Deep Neural Networks are Easily Fooled
- 8. INQ 论文解读：Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights
- 9. ECCV 2020 的对抗相关论文（对抗生成、对抗攻击）
- 10. Gated-SCNN: Gated Shape CNNs for Semantic Segmentation——论文阅读理解