1 citations · 2 across the 6 of their papers we have counts for
6 papers
Unveiling Key Aspects of Fine-Tuning in Sentence Embeddings: A Representation Rank Analysis
Euna Jung, Jaeill Kim, Jungmin Ko +2
The latest advancements in unsupervised learning of sentence embeddings predominantly involve employing contrastive learning-based (CL-based) fine-tuning over pre-trained language…
Improving Forward Compatibility in Class Incremental Learning by Increasing Representation Rank and Feature Richness
Jaeill Kim, Wonseok Lee, Moonjung Eo +1
Class Incremental Learning (CIL) constitutes a pivotal subfield within continual learning, aimed at enabling models to progressively learn new classification tasks while retaining…
Enhancing Contrastive Learning with Efficient Combinatorial Positive Pairing
Jaeill Kim, Duhun Hwang, Eunjung Lee +3
In the past few years, contrastive learning has played a central role for the success of visual unsupervised representation learning. Around the same time, high-performance non-con…
Towards a Rigorous Analysis of Mutual Information in Contrastive Learning
Kyungeun Lee, Jaeill Kim, Suhyun Kang +1
Contrastive learning has emerged as a cornerstone in recent achievements of unsupervised representation learning. Its primary paradigm involves an instance discrimination task with…
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution
Jaeill Kim, Suhyun Kang, Duhun Hwang +2
Since the introduction of deep learning, a wide scope of representation properties, such as decorrelation, whitening, disentanglement, rank, isotropy, and mutual information, have…
DR.CPO: Diversified and Realistic 3D Augmentation via Iterative Construction, Random Placement, and HPR Occlusion
Jungwook Shin, Jaeill Kim, Kyungeun Lee +2
In autonomous driving, data augmentation is commonly used for improving 3D object detection. The most basic methods include insertion of copied objects and rotation and scaling of…