44 citations · 88 across the 13 of their papers we have counts for
5 papers · 1 filter
CoSDA: Continual Source-Free Domain Adaptation
Haozhe Feng, Zhaorui Yang, Hesun Chen +5
Without access to the source data, source-free domain adaptation (SFDA) transfers knowledge from a source-domain trained model to target domains. Recently, SFDA has gained populari…
BAFFLE: A Baseline of Backpropagation-Free Federated Learning
Haozhe Feng, Tianyu Pang, Chao Du +3
Federated learning (FL) is a general principle for decentralized clients to train a server model collectively without sharing local data. FL is a promising framework with practical…
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations
Hao-Zhe Feng, Kezhi Kong, Minghao Chen +3
Semi-supervised variational autoencoders (VAEs) have obtained strong results, but have also encountered the challenge that good ELBO values do not always imply accurate inference r…
KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation
Hao-Zhe Feng, Zhaoyang You, Minghao Chen +5
Conventional unsupervised multi-source domain adaptation (UMDA) methods assume all source domains can be accessed directly. This neglects the privacy-preserving policy, that is, al…
An Interactive Insight Identification and Annotation Framework for Power Grid Pixel Maps using DenseU-Hierarchical VAE
Tianye Zhang, Haozhe Feng, Zexian Chen +4
Insights in power grid pixel maps (PGPMs) refer to important facility operating states and unexpected changes in the power grid. Identifying insights helps analysts understand the…