14 citations · 48 across the 7 of their papers we have counts for
7 papers
Text-guided Foundation Model Adaptation for Pathological Image Classification
Yunkun Zhang, Jin Gao, Mu Zhou +4
The recent surge of foundation models in computer vision and natural language processing opens up perspectives in utilizing multi-modal clinical data to train large models with str…
Towards General Purpose Medical AI: Continual Learning Medical Foundation Model
Huahui Yi, Ziyuan Qin, Qicheng Lao +5
Inevitable domain and task discrepancies in real-world scenarios can impair the generalization performance of the pre-trained deep models for medical data. Therefore, we audaciousl…
Back to the Source: Diffusion-Driven Test-Time Adaptation
Jin Gao, Jialing Zhang, Xihui Liu +3
Test-time adaptation harnesses test inputs to improve the accuracy of a model trained on source data when tested on shifted target data. Existing methods update the source model by…
Contrastive Test-Time Adaptation
Dian Chen, Dequan Wang, Trevor Darrell +1
Test-time adaptation is a special setting of unsupervised domain adaptation where a trained model on the source domain has to adapt to the target domain without accessing source da…
On-target Adaptation
Dequan Wang, Shaoteng Liu, Sayna Ebrahimi +2
Domain adaptation seeks to mitigate the shift between training on the \emph{source} domain and testing on the \emph{target} domain. Most adaptation methods rely on the source data…
Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks
Dequan Wang, An Ju, Evan Shelhamer +2
Adversarial attacks optimize against models to defeat defenses. Existing defenses are static, and stay the same once trained, even while attacks change. We argue that models should…
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
Jianfei Chen, Lianmin Zheng, Zhewei Yao +4
The increasing size of neural network models has been critical for improvements in their accuracy, but device memory is not growing at the same rate. This creates fundamental chall…