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Found 13 papers, 9 papers with code
Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection
In the open world, detecting out-of-distribution (OOD) data, whose labels are disjoint with those of in-distribution (ID) samples, is important for reliable deep neural networks (DNNs).
Revisiting Random Weight Perturbation for Efficiently Improving Generalization
Improving the generalization ability of modern deep neural networks (DNNs) is a fundamental challenge in machine learning.
Remaining-data-free Machine Unlearning by Suppressing Sample Contribution
We first theoretically discover that sample's contribution during the process will reflect in the learned model's sensitivity to it.
Online Continual Learning via Logit Adjusted Softmax
Online continual learning is a challenging problem where models must learn from a non-stationary data stream while avoiding catastrophic forgetting.
Low-Dimensional Gradient Helps Out-of-Distribution Detection
To bridge this gap, in this paper, we conduct a comprehensive investigation into leveraging the entirety of gradient information for OOD detection.
Efficient Generalization Improvement Guided by Random Weight Perturbation
To fully uncover the great potential of deep neural networks (DNNs), various learning algorithms have been developed to improve the model's generalization ability.
On Multi-head Ensemble of Smoothed Classifiers for Certified Robustness
Randomized Smoothing (RS) is a promising technique for certified robustness, and recently in RS the ensemble of multiple deep neural networks (DNNs) has shown state-of-the-art performances.
Unifying Gradients to Improve Real-world Robustness for Deep Networks
The wide application of deep neural networks (DNNs) demands an increasing amount of attention to their real-world robustness, i. e., whether a DNN resists black-box adversarial attacks, among which score-based query attacks (SQAs) are most threatening since they can effectively hurt a victim network with the only access to model outputs.
Trainable Weight Averaging: A General Approach for Subspace Training
Training deep neural networks (DNNs) in low-dimensional subspaces is a promising direction for achieving efficient training and better generalization performance.
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks
The score-based query attacks (SQAs) pose practical threats to deep neural networks by crafting adversarial perturbations within dozens of queries, only using the model's output scores.
Subspace Adversarial Training
Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust.
Towards Robust Neural Networks via Orthogonal Diversity
In this way, the proposed DIO augments the model and enhances the robustness of DNN itself as the learned features can be corrected by these mutually-orthogonal paths.
Learn Robust Features via Orthogonal Multi-Path
To defend adversarial attacks, we design a block containing multiple paths to learn robust features and the parameters of these paths are required to be orthogonal with each other.
