113 citations · 282 across the 27 of their papers we have counts for
9 papers · 1 filter
Learning to Augment via Implicit Differentiation for Domain Generalization
Tingwei Wang, Da Li, Kaiyang Zhou +2
Machine learning models are intrinsically vulnerable to domain shift between training and testing data, resulting in poor performance in novel domains. Domain generalization (DG) a…
Prediction Calibration for Generalized Few-shot Semantic Segmentation
Zhihe Lu, Sen He, Da Li +2
Generalized Few-shot Semantic Segmentation (GFSS) aims to segment each image pixel into either base classes with abundant training examples or novel classes with only a handful of…
Robust Target Training for Multi-Source Domain Adaptation
Zhongying Deng, Da Li, Yi-Zhe Song +1
Given multiple labeled source domains and a single target domain, most existing multi-source domain adaptation (MSDA) models are trained on data from all domains jointly in one ste…
Attacking Adversarial Defences by Smoothing the Loss Landscape
Panagiotis Eustratiadis, Henry Gouk, Da Li +1
This paper investigates a family of methods for defending against adversarial attacks that owe part of their success to creating a noisy, discontinuous, or otherwise rugged loss la…
Feed-Forward Latent Domain Adaptation
Ondrej Bohdal, Da Li, Shell Xu Hu +1
We study a new highly-practical problem setting that enables resource-constrained edge devices to adapt a pre-trained model to their local data distributions. Recognizing that devi…
Fisher SAM: Information Geometry and Sharpness Aware Minimisation
Minyoung Kim, Da Li, Shell Xu Hu +1
Recent sharpness-aware minimisation (SAM) is known to find flat minima which is beneficial for better generalisation with improved robustness. SAM essentially modifies the loss fun…