CVPR20-paper-to-read

一、Adversarial Example

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1.1 Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distance

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1.2 One Man's Trash Is Another Man's Treasure: Resisting Adversarial Examples by Adversarial Examples

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1.3 ColorFool: Semantic Adversarial Colorization

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1.4 Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking

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1.5 Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning

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1.6 Efficient Adversarial Training with Transferable Adversarial Examples

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1.7 Modeling Biological Immunity to Adversarial Examples

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1.8 Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes

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1.9 (Oral)A Self-supervised Approach for Adversarial Robustness

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1.10 When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks

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1.11 Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder

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1.12 Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory

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1.13 LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks

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1.15 Polishing Decision-Based Adversarial Noise With a Customized Sampling

1.16 Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations

1.17 Single-Step Adversarial Training With Dropout Scheduling

1.18 Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization

1.19 Boosting the Transferability of Adversarial Samples via Attention

1.20 Learn2Perturb: An End-to-End Feature Perturbation Learning to Improve Adversarial Robustness

1.21 On Isometry Robustness of Deep 3D Point Cloud Models Under Adversarial Attacks

1.22 Adversarial Examples Improve Image Recognition

1.23 Enhancing Cross-Task Black-Box Transferability of Adversarial Examples With Dispersion Reduction

1.24 Adversarial Camouflage: Hiding Physical-World Attacks With Natural Styles

1.25 Benchmarking Adversarial Robustness on Image Classification

1.26 DaST: Data-Free Substitute Training for Adversarial Attacks

1.27 Ensemble Generative Cleaning With Feedback Loops for Defending Adversarial Attacks

1.28 Exploiting Joint Robustness to Adversarial Perturbations

1.29 GeoDA: A Geometric Framework for Black-Box Adversarial Attacks

1.30 What Machines See Is Not What They Get: Fooling Scene Text Recognition Models With Adversarial Text Images

1.31 Physically Realizable Adversarial Examples for LiDAR Object Detection

1.32 One-Shot Adversarial Attacks on Visual Tracking With Dual Attention

1.33 Defending and Harnessing the Bit-Flip Based Adversarial Weight Attack

1.34 Understanding Adversarial Examples From the Mutual Influence of Images and Perturbations

1.35 Robust Superpixel-Guided Attentional Adversarial Attack

1.36 ILFO: Adversarial Attack on Adaptive Neural Networks

1.37 PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving

1.38 Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors

二、Meta-learning

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2.1 Learning to Forget for Meta-Learning

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2.2 Training Noise-Robust Deep Neural Networks via Meta-Learning

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2.3 Meta-Transfer Learning for Zero-Shot Super-Resolution

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