Search Results for author: Sungwoong Kim

Found 18 papers, 11 papers with code

Contrastive Regularization for Semi-Supervised Learning

1 code implementation17 Jan 2022 Doyup Lee, Sungwoong Kim, Ildoo Kim, Yeongjae Cheon, Minsu Cho, Wook-Shin Han

Consistency regularization on label predictions becomes a fundamental technique in semi-supervised learning, but it still requires a large number of training iterations for high performance.

Selective Token Generation for Few-shot Language Modeling

no code implementations29 Sep 2021 DaeJin Jo, Taehwan Kwon, Sungwoong Kim, Eun-Sol Kim

Therefore, in this work, we develop a novel additive learning algorithm based on reinforcement learning (RL) for few-shot natural language generation (NLG) tasks.

Data-to-Text Generation Language Modelling +3

Hybrid Generative-Contrastive Representation Learning

1 code implementation11 Jun 2021 Saehoon Kim, Sungwoong Kim, Juho Lee

On the other hand, the generative pre-training directly estimates the data distribution, so the representations tend to be robust but not optimal for discriminative tasks.

Contrastive Learning Representation Learning

Spatially Consistent Representation Learning

2 code implementations CVPR 2021 Byungseok Roh, Wuhyun Shin, Ildoo Kim, Sungwoong Kim

While these contrastive methods mainly focus on generating invariant global representations at the image-level under semantic-preserving transformations, they are prone to overlook spatial consistency of local representations and therefore have a limitation in pretraining for localization tasks such as object detection and instance segmentation.

Contrastive Learning Image Classification +5

Visual Concept Reasoning Networks

no code implementations26 Aug 2020 Taesup Kim, Sungwoong Kim, Yoshua Bengio

It approximates sparsely connected networks by explicitly defining multiple branches to simultaneously learn representations with different visual concepts or properties.

Action Recognition Image Classification +3

AutoCLINT: The Winning Method in AutoCV Challenge 2019

1 code implementation9 May 2020 Woonhyuk Baek, Ildoo Kim, Sungwoong Kim, Sungbin Lim

NeurIPS 2019 AutoDL challenge is a series of six automated machine learning competitions.

Data Augmentation

torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models

3 code implementations21 Apr 2020 Chiheon Kim, Heungsub Lee, Myungryong Jeong, Woonhyuk Baek, Boogeon Yoon, Ildoo Kim, Sungbin Lim, Sungwoong Kim

We design and implement a ready-to-use library in PyTorch for performing micro-batch pipeline parallelism with checkpointing proposed by GPipe (Huang et al., 2019).

Spatially Attentive Output Layer for Image Classification

no code implementations CVPR 2020 Ildoo Kim, Woonhyuk Baek, Sungwoong Kim

In this paper, we propose a novel spatial output layer on top of the existing convolutional feature maps to explicitly exploit the location-specific output information.

Classification General Classification +1

Mining GOLD Samples for Conditional GANs

1 code implementation NeurIPS 2019 Sangwoo Mo, Chiheon Kim, Sungwoong Kim, Minsu Cho, Jinwoo Shin

Conditional generative adversarial networks (cGANs) have gained a considerable attention in recent years due to its class-wise controllability and superior quality for complex generation tasks.

Active Learning

Scalable Neural Architecture Search for 3D Medical Image Segmentation

no code implementations13 Jun 2019 Sungwoong Kim, Ildoo Kim, Sungbin Lim, Woonhyuk Baek, Chiheon Kim, Hyungjoo Cho, Boogeon Yoon, Taesup Kim

In this paper, a neural architecture search (NAS) framework is proposed for 3D medical image segmentation, to automatically optimize a neural architecture from a large design space.

Medical Image Segmentation Neural Architecture Search +1

Edge-labeling Graph Neural Network for Few-shot Learning

3 code implementations CVPR 2019 Jongmin Kim, Taesup Kim, Sungwoong Kim, Chang D. Yoo

In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning.

Few-Shot Image Classification

Fast AutoAugment

10 code implementations NeurIPS 2019 Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, Sungwoong Kim

Data augmentation is an essential technique for improving generalization ability of deep learning models.

Image Augmentation Image Classification

Bayesian Model-Agnostic Meta-Learning

2 code implementations NeurIPS 2018 Taesup Kim, Jaesik Yoon, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, Sungjin Ahn

Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem.

Active Learning Image Classification +3

A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems

no code implementations2 Apr 2014 Jörg H. Kappes, Bjoern Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Thorben Kröger, Jan Lellmann, Nikos Komodakis, Bogdan Savchynskyy, Carsten Rother

However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.

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