52 citations · 56 across the 4 of their papers we have counts for
8 papers
Towards Adversarial Robustness via Transductive Learning
Jiefeng Chen, Yang Guo, Xi Wu +4
There has been emerging interest to use transductive learning for adversarial robustness (Goldwasser et al., NeurIPS 2020; Wu et al., ICML 2020). Compared to traditional "test-time…
Hypothesis Disparity Regularized Mutual Information Maximization
Qicheng Lao, Xiang Jiang, Mohammad Havaei
We propose a hypothesis disparity regularized mutual information maximization~(HDMI) approach to tackle unsupervised hypothesis transfer -- as an effort towards unifying hypothesis…
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
Xiang Jiang, Qicheng Lao, Stan Matwin +1
We present an approach for unsupervised domain adaptation---with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution s…
Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay
Qicheng Lao, Xiang Jiang, Mohammad Havaei +1
Learning in non-stationary environments is one of the biggest challenges in machine learning. Non-stationarity can be caused by either task drift, i.e., the drift in the conditiona…
FoCL: Feature-Oriented Continual Learning for Generative Models
Qicheng Lao, Mehrzad Mortazavi, Marzieh Tahaei +3
In this paper, we propose a general framework in continual learning for generative models: Feature-oriented Continual Learning (FoCL). Unlike previous works that aim to solve the c…
Dual Adversarial Inference for Text-to-Image Synthesis
Qicheng Lao, Mohammad Havaei, Ahmad Pesaranghader +3
Synthesizing images from a given text description involves engaging two types of information: the content, which includes information explicitly described in the text (e.g., color,…