5 citations · 6 across the 4 of their papers we have counts for
4 papers
Towards Flexible Inductive Bias via Progressive Reparameterization Scheduling
Yunsung Lee, Gyuseong Lee, Kwangrok Ryoo +3
There are two de facto standard architectures in recent computer vision: Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Strong inductive biases of convolution…
ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization
Jiwon Kim, Youngjo Min, Daehwan Kim +4
We present a novel semi-supervised learning framework that intelligently leverages the consistency regularization between the model's predictions from two strongly-augmented views…
Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels
Jiwon Kim, Kwangrok Ryoo, Junyoung Seo +4
Establishing dense correspondences across semantically similar images remains a challenging task due to the significant intra-class variations and background clutters. Traditionall…
AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning
Jiwon Kim, Kwangrok Ryoo, Gyuseong Lee +5
Semi-supervised learning (SSL) has recently proven to be an effective paradigm for leveraging a huge amount of unlabeled data while mitigating the reliance on large labeled data. C…