Paper list
A complete list of papers I’ve read.
2015
Deep Residual Learning for Image Recognition
2016
Aggregated Residual Transformations for Deep Neural Networks
Densely Connected Convolutional Networks
Wide Residual Networks
2017
Towards Deep Learning Models Resistant to Adversarial Attacks
2018
DARTS: Differentiable Architecture Search
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
Is Robustness the Cost of Accuracy? - A Comprehensive Study on the Robustness of 18 Deep Image Classification
2019
Class-Balanced Loss Based on Effective Number of Samples
Intriguing Properties of Adversarial Training at Scale
When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks
2020
Accurate Anchor Free Tracking
Adversarially Robust Neural Architectures
Designing Network Design Spaces
Fast is better than free: Revisiting adversarial training
On Adversarial Robustness: A Neural Architecture Search perspective
Smooth Adversarial Training
Understanding and Improving Fast Adversarial Training
2021
A novel DNN tracking algorithm for structural system identification
AdvRush: Searching for Adversarially Robust Neural Architectures
Deep Long-Tailed Learning: A Survey
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks
Parameterizing Activation Functions for Adversarial Robustness
RobustART: Benchmarking Robustness on Architecture Design and Training Techniques
Subspace Adversarial Training
2022
A ConvNet for the 2020s
A Light Recipe to Train Robust Vision Transformers
Are Transformers more robust than CNNs?
DetectorDetective: Investigating the Effects of Adversarial Examples on Object Detectors
Skelevision: Towards adversarial resiliency of person tracking with multi-task learning
2023
RobArch: Designing Robust Architectures against Adversarial Attacks