12 citations · 22 across the 5 of their papers we have counts for
5 papers
Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment
Dong Hoon Lee, Sungik Choi, Hyunwoo Kim +1
This paper proposes Mutual Information Regularized Assignment (MIRA), a pseudo-labeling algorithm for unsupervised representation learning inspired by information maximization. We…
Few-Example Clustering via Contrastive Learning
Minguk Jang, Sae-Young Chung
We propose Few-Example Clustering (FEC), a novel algorithm that performs contrastive learning to cluster few examples. Our method is composed of the following three steps: (1) gene…
Test-Time Adaptation via Self-Training with Nearest Neighbor Information
Minguk Jang, Sae-Young Chung, Hye Won Chung
Test-time adaptation (TTA) aims to adapt a trained classifier using online unlabeled test data only, without any information related to the training procedure. Most existing TTA me…
Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification
Dong Hoon Lee, Sae-Young Chung
We propose unsupervised embedding adaptation for the downstream few-shot classification task. Based on findings that deep neural networks learn to generalize before memorizing, we…
Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics Mixture
Suyoung Lee, Sae-Young Chung
The generalization ability of most meta-reinforcement learning (meta-RL) methods is largely limited to test tasks that are sampled from the same distribution used to sample trainin…