113 citations · 139 across the 7 of their papers we have counts for
10 papers · 1 filter
Neural Fine-Tuning Search for Few-Shot Learning
Panagiotis Eustratiadis, Łukasz Dudziak, Da Li +1
In few-shot recognition, a classifier that has been trained on one set of classes is required to rapidly adapt and generalize to a disjoint, novel set of classes. To that end, rece…
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…
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…
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference
Shell Xu Hu, Da Li, Jan Stühmer +2
Few-shot learning (FSL) is an important and topical problem in computer vision that has motivated extensive research into numerous methods spanning from sophisticated meta-learning…
Sequential Learning for Domain Generalization
Da Li, Yongxin Yang, Yi-Zhe Song +1
In this paper we propose a sequential learning framework for Domain Generalization (DG), the problem of training a model that is robust to domain shift by design. Various DG approa…
Online Meta-Learning for Multi-Source and Semi-Supervised Domain Adaptation
Da Li, Timothy Hospedales
Domain adaptation (DA) is the topical problem of adapting models from labelled source datasets so that they perform well on target datasets where only unlabelled or partially label…