Search Results for author: Hankook Lee

Found 13 papers, 11 papers with code

Guiding Energy-based Models via Contrastive Latent Variables

1 code implementation6 Mar 2023 Hankook Lee, Jongheon Jeong, Sejun Park, Jinwoo Shin

To enable the joint training of EBM and CRL, we also design a new class of latent-variable EBMs for learning the joint density of data and the contrastive latent variable.

Representation Learning

STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables

1 code implementation2 Mar 2023 Jaehyun Nam, Jihoon Tack, Kyungmin Lee, Hankook Lee, Jinwoo Shin

Learning with few labeled tabular samples is often an essential requirement for industrial machine learning applications as varieties of tabular data suffer from high annotation costs or have difficulties in collecting new samples for novel tasks.

Few-Shot Learning

Meta-Learning with Self-Improving Momentum Target

1 code implementation11 Oct 2022 Jihoon Tack, Jongjin Park, Hankook Lee, Jaeho Lee, Jinwoo Shin

The idea of using a separately trained target model (or teacher) to improve the performance of the student model has been increasingly popular in various machine learning domains, and meta-learning is no exception; a recent discovery shows that utilizing task-wise target models can significantly boost the generalization performance.

Knowledge Distillation Meta-Learning +1

Patch-level Representation Learning for Self-supervised Vision Transformers

1 code implementation CVPR 2022 Sukmin Yun, Hankook Lee, Jaehyung Kim, Jinwoo Shin

Despite its simplicity, we demonstrate that it can significantly improve the performance of existing SSL methods for various visual tasks, including object detection and semantic segmentation.

Instance Segmentation object-detection +4

Improving Transferability of Representations via Augmentation-Aware Self-Supervision

1 code implementation NeurIPS 2021 Hankook Lee, Kibok Lee, Kimin Lee, Honglak Lee, Jinwoo Shin

Recent unsupervised representation learning methods have shown to be effective in a range of vision tasks by learning representations invariant to data augmentations such as random cropping and color jittering.

Representation Learning Transfer Learning

PASS: Patch-Aware Self-Supervision for Vision Transformer

no code implementations29 Sep 2021 Sukmin Yun, Hankook Lee, Jaehyung Kim, Jinwoo Shin

This paper aims to improve their performance further by utilizing the architectural advantages of the underlying neural network, as the current state-of-the-art visual pretext tasks for self-supervised learning do not enjoy the benefit, i. e., they are architecture-agnostic.

object-detection Object Detection +3

Self-Improved Retrosynthetic Planning

1 code implementation9 Jun 2021 Junsu Kim, Sungsoo Ahn, Hankook Lee, Jinwoo Shin

Our main idea is based on a self-improving procedure that trains the model to imitate successful trajectories found by itself.

Multi-step retrosynthesis

RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning

no code implementations3 May 2021 Hankook Lee, Sungsoo Ahn, Seung-Woo Seo, You Young Song, Eunho Yang, Sung-Ju Hwang, Jinwoo Shin

Retrosynthesis, of which the goal is to find a set of reactants for synthesizing a target product, is an emerging research area of deep learning.

Contrastive Learning

Guiding Deep Molecular Optimization with Genetic Exploration

2 code implementations NeurIPS 2020 Sungsoo Ahn, Junsu Kim, Hankook Lee, Jinwoo Shin

De novo molecular design attempts to search over the chemical space for molecules with the desired property.

Imitation Learning

Self-supervised Label Augmentation via Input Transformations

1 code implementation ICML 2020 Hankook Lee, Sung Ju Hwang, Jinwoo Shin

Our main idea is to learn a single unified task with respect to the joint distribution of the original and self-supervised labels, i. e., we augment original labels via self-supervision of input transformation.

Data Augmentation imbalanced classification +2

Learning What and Where to Transfer

4 code implementations15 May 2019 Yunhun Jang, Hankook Lee, Sung Ju Hwang, Jinwoo Shin

To address the issue, we propose a novel transfer learning approach based on meta-learning that can automatically learn what knowledge to transfer from the source network to where in the target network.

Meta-Learning Small Data Image Classification +1

Anytime Neural Prediction via Slicing Networks Vertically

1 code implementation7 Jul 2018 Hankook Lee, Jinwoo Shin

This is remarkable due to their simplicity and effectiveness, but training many thin sub-networks jointly faces a new challenge on training complexity.

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