166 citations · 644 across the 58 of their papers we have counts for
10 papers · 1 filter
A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data
Xianfeng Li, Weijie Chen, Di Xie +4
Unsupervised domain adaptation (UDA) assumes that source and target domain data are freely available and usually trained together to reduce the domain gap. However, considering the…
Run Away From your Teacher: Understanding BYOL by a Novel Self-Supervised Approach
Haizhou Shi, Dongliang Luo, Siliang Tang +2
Recently, a newly proposed self-supervised framework Bootstrap Your Own Latent (BYOL) seriously challenges the necessity of negative samples in contrastive learning frameworks. BYO…
Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation
Guoliang Kang, Yunchao Wei, Yi Yang +2
Domain adaptive semantic segmentation aims to train a model performing satisfactory pixel-level predictions on the target with only out-of-domain (source) annotations. The conventi…
Federated Unsupervised Representation Learning
Fengda Zhang, Kun Kuang, Zhaoyang You +6
To leverage enormous unlabeled data on distributed edge devices, we formulate a new problem in federated learning called Federated Unsupervised Representation Learning (FURL) to le…
Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling
Jiacheng Li, Siliang Tang, Juncheng Li +4
Visual Storytelling~(VIST) is a task to tell a narrative story about a certain topic according to the given photo stream. The existing studies focus on designing complex models, wh…
Stable Prediction via Leveraging Seed Variable
Kun Kuang, Bo Li, Peng Cui +4
In this paper, we focus on the problem of stable prediction across unknown test data, where the test distribution is agnostic and might be totally different from the training one.…
Balance-Subsampled Stable Prediction
Kun Kuang, Hengtao Zhang, Fei Wu +2
In machine learning, it is commonly assumed that training and test data share the same population distribution. However, this assumption is often violated in practice because the s…
Learning Decomposed Representation for Counterfactual Inference
Anpeng Wu, Kun Kuang, Junkun Yuan +5
The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing. Most of the previous methods realized confounder balancin…
Counterfactual Samples Synthesizing for Robust Visual Question Answering
Long Chen, Xin Yan, Jun Xiao +3
Despite Visual Question Answering (VQA) has realized impressive progress over the last few years, today's VQA models tend to capture superficial linguistic correlations in the trai…
Bi-Decoder Augmented Network for Neural Machine Translation
Boyuan Pan, Yazheng Yang, Zhou Zhao +2
Neural Machine Translation (NMT) has become a popular technology in recent years, and the encoder-decoder framework is the mainstream among all the methods. It's obvious that the q…