144 citations · 865 across the 49 of their papers we have counts for
7 papers · 1 filter
Test-time Adaptation in the Dynamic World with Compound Domain Knowledge Management
Junha Song, Kwanyong Park, InKyu Shin +3
Prior to the deployment of robotic systems, pre-training the deep-recognition models on all potential visual cases is infeasible in practice. Hence, test-time adaptation (TTA) allo…
On the Pros and Cons of Momentum Encoder in Self-Supervised Visual Representation Learning
Trung Pham, Chaoning Zhang, Axi Niu +2
Exponential Moving Average (EMA or momentum) is widely used in modern self-supervised learning (SSL) approaches, such as MoCo, for enhancing performance. We demonstrate that such m…
A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond
Chaoning Zhang, Chenshuang Zhang, Junha Song +3
Masked autoencoders are scalable vision learners, as the title of MAE \cite{he2022masked}, which suggests that self-supervised learning (SSL) in vision might undertake a similar tr…
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness
Chaoning Zhang, Kang Zhang, Chenshuang Zhang +4
Adversarial training (AT) for robust representation learning and self-supervised learning (SSL) for unsupervised representation learning are two active research fields. Integrating…
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning
Huy Q. Le, Minh N. H. Nguyen, Shashi Raj Pandey +2
In a practical setting, how to enable robust Federated Learning (FL) systems, both in terms of generalization and personalization abilities, is one important research question. It…
Practical No-box Adversarial Attacks with Training-free Hybrid Image Transformation
Qilong Zhang, Youheng Sun, Chaoning Zhang +4
In recent years, the adversarial vulnerability of deep neural networks (DNNs) has raised increasing attention. Among all the threat models, no-box attacks are the most practical bu…
Fast Adversarial Training with Noise Augmentation: A Unified Perspective on RandStart and GradAlign
Axi Niu, Kang Zhang, Chaoning Zhang +4
PGD-based and FGSM-based are two popular adversarial training (AT) approaches for obtaining adversarially robust models. Compared with PGD-based AT, FGSM-based one is significantly…