activity
20202023
most citedMUSCLE: Strengthening Semi-Supervised Learning Via Concurrent Unsupervised Learning Using Mutual Information Maximization

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20231 cited

A Critical View of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignment

Hanchen Xie, Jiageng Zhu, Mahyar Khayatkhoei +3

Dynamics prediction, which is the problem of predicting future states of scene objects based on current and prior states, is drawing increasing attention as an instance of learning…

cs.LG2022

SW-VAE: Weakly Supervised Learn Disentangled Representation Via Latent Factor Swapping

Jiageng Zhu, Hanchen Xie, Wael Abd-Almageed

Representation disentanglement is an important goal of representation learning that benefits various downstream tasks. To achieve this goal, many unsupervised learning representati…

cs.LG2022

Weakly Supervised Invariant Representation Learning Via Disentangling Known and Unknown Nuisance Factors

Jiageng Zhu, Hanchen Xie, Wael Abd-Almageed

Disentangled and invariant representations are two critical goals of representation learning and many approaches have been proposed to achieve either one of them. However, those tw…

cs.CV2021

Partner-Assisted Learning for Few-Shot Image Classification

Jiawei Ma, Hanchen Xie, Guangxing Han +3

Few-shot Learning has been studied to mimic human visual capabilities and learn effective models without the need of exhaustive human annotation. Even though the idea of meta-learn…

cs.LG20202 cited

MUSCLE: Strengthening Semi-Supervised Learning Via Concurrent Unsupervised Learning Using Mutual Information Maximization

Hanchen Xie, Mohamed E. Hussein, Aram Galstyan +1

Deep neural networks are powerful, massively parameterized machine learning models that have been shown to perform well in supervised learning tasks. However, very large amounts of…