Search Results for author: Jaegul Choo

Found 65 papers, 24 papers with code

Novel Natural Language Summarization of Program Code via Leveraging Multiple Input Representations

no code implementations Findings (EMNLP) 2021 Fuxiang Chen, Mijung Kim, Jaegul Choo

To tackle this problem, previous work on code summarization, the task of automatically generating code description given a piece of code reported that an auxiliary learning model trained to produce API (Application Programming Interface) embeddings showed promising results when applied to a downstream, code summarization model.

Auxiliary Learning Code Summarization +1

DraftRec: Personalized Draft Recommendation for Winning in Multi-Player Online Battle Arena Games

1 code implementation27 Apr 2022 Hojoon Lee, Dongyoon Hwang, Hyunseung Kim, Byungkun Lee, Jaegul Choo

To alleviate this problem, we propose DraftRec, a novel hierarchical model which recommends characters by considering each player's champion preferences and the interaction between the players.

League of Legends

3D-GIF: 3D-Controllable Object Generation via Implicit Factorized Representations

no code implementations12 Mar 2022 Minsoo Lee, Chaeyeon Chung, Hojun Cho, Minjung Kim, Sanghun Jung, Jaegul Choo, Minhyuk Sung

While NeRF-based 3D-aware image generation methods enable viewpoint control, limitations still remain to be adopted to various 3D applications.

Image Generation

CG-NeRF: Conditional Generative Neural Radiance Fields

no code implementations7 Dec 2021 Kyungmin Jo, Gyumin Shim, Sanghun Jung, Soyoung Yang, Jaegul Choo

While recent NeRF-based generative models achieve the generation of diverse 3D-aware images, these approaches have limitations when generating images that contain user-specified characteristics.

3D-Aware Image Synthesis Face Generation

Data Augmentation using Random Image Cropping for High-resolution Virtual Try-On (VITON-CROP)

no code implementations16 Nov 2021 Taewon Kang, Sunghyun Park, Seunghwan Choi, Jaegul Choo

Image-based virtual try-on provides the capacity to transfer a clothing item onto a photo of a given person, which is usually accomplished by warping the item to a given human pose and adjusting the warped item to the person.

Data Augmentation Image Cropping +1

AnimeCeleb: Large-Scale Animation CelebFaces Dataset via Controllable 3D Synthetic Models

no code implementations15 Nov 2021 Kangyeol Kim, Sunghyun Park, Jaeseong Lee, Sunghyo Chung, Junsoo Lee, Jaegul Choo

In this paper, we present a large-scale animation celebfaces dataset (AnimeCeleb) via controllable synthetic animation models to boost research on the animation face domain.


AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain

1 code implementation EMNLP 2021 Jimin Hong, Taehee Kim, Hyesu Lim, Jaegul Choo

During the fine-tuning phase of transfer learning, the pretrained vocabulary remains unchanged, while model parameters are updated.

Language Modelling Transfer Learning

Improving Face Recognition with Large Age Gaps by Learning to Distinguish Children

1 code implementation22 Oct 2021 Jungsoo Lee, Jooyeol Yun, Sunghyun Park, Yonggyu Kim, Jaegul Choo

Despite the unprecedented improvement of face recognition, existing face recognition models still show considerably low performances in determining whether a pair of child and adult images belong to the same identity.

Face Recognition

Natural Attribute-based Shift Detection

no code implementations18 Oct 2021 Jeonghoon Park, Jimin Hong, Radhika Dua, Daehoon Gwak, Yixuan Li, Jaegul Choo, Edward Choi

Despite the impressive performance of deep networks in vision, language, and healthcare, unpredictable behaviors on samples from the distribution different than the training distribution cause severe problems in deployment.

OOD Detection

Mining Multi-Label Samples from Single Positive Labels

no code implementations29 Sep 2021 Youngin Cho, Daejin Kim, Jaegul Choo

Hence, we explore the practical setting called single positive setting, where each data instance is annotated by only one positive label with no explicit negative labels.

Distance-Based Background Class Regularization for Open-Set Recognition

no code implementations29 Sep 2021 Wonwoo Cho, Jaegul Choo

In this paper, we propose a novel distance-based BCR method suitable for OSR, which limits the feature space of known-class data in a class-wise manner and then makes background-class samples located far away from the limited feature space.

Open Set Learning

Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift

no code implementations ICLR 2022 Taesung Kim, Jinhee Kim, Yunwon Tae, Cheonbok Park, Jang-Ho Choi, Jaegul Choo

The former normalizes the input to fix its distribution in terms of the mean and variance, while the latter returns the output to the original distribution.

Time Series Time Series Forecasting

Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel

no code implementations29 Sep 2021 Daehoon Gwak, Gyubok Lee, Jaehoon Lee, Jaesik Choi, Jaegul Choo, Edward Choi

To address this, we introduce a new neural stochastic processes, Decoupled Kernel Neural Processes (DKNPs), which explicitly learn a separate mean and kernel function to directly model the covariance between output variables in a data-driven manner.

Gaussian Processes

BiaSwap: Removing dataset bias with bias-tailored swapping augmentation

no code implementations ICCV 2021 Eungyeup Kim, Jihyeon Lee, Jaegul Choo

Although previous approaches pre-define the type of dataset bias to prevent the network from learning it, recognizing the bias type in the real dataset is often prohibitive.

Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation

1 code implementation ICCV 2021 Sanghun Jung, Jungsoo Lee, Daehoon Gwak, Sungha Choi, Jaegul Choo

However, the distribution of max logits of each predicted class is significantly different from each other, which degrades the performance of identifying unexpected objects in urban-scene segmentation.

Anomaly Detection Scene Segmentation

Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images

1 code implementation ACL 2021 Nyoungwoo Lee, Suwon Shin, Jaegul Choo, Ho-Jin Choi, Sung-Hyun Myaeng

In multi-modal dialogue systems, it is important to allow the use of images as part of a multi-turn conversation.

Deep Edge-Aware Interactive Colorization against Color-Bleeding Effects

no code implementations ICCV 2021 Eungyeup Kim, Sanghyeon Lee, Jeonghoon Park, Somi Choi, Choonghyun Seo, Jaegul Choo

Deep neural networks for automatic image colorization often suffer from the color-bleeding artifact, a problematic color spreading near the boundaries between adjacent objects.


Learning Debiased Representation via Disentangled Feature Augmentation

1 code implementation NeurIPS 2021 Jungsoo Lee, Eungyeup Kim, Juyoung Lee, Jihyeon Lee, Jaegul Choo

To this end, our method learns the disentangled representation of (1) the intrinsic attributes (i. e., those inherently defining a certain class) and (2) bias attributes (i. e., peripheral attributes causing the bias), from a large number of bias-aligned samples, the bias attributes of which have strong correlation with the target variable.

Data Augmentation Image Classification

Not Just Compete, but Collaborate: Local Image-to-Image Translation via Cooperative Mask Prediction

no code implementations CVPR 2021 Daejin Kim, Mohammad Azam Khan, Jaegul Choo

While the existing cycle-consistency loss ensures that the image can be translated back, our approach makes the model further preserve the attribute-irrelevant regions even in a single translation to another domain by using the Grad-CAM output computed from the discriminator.

Image-to-Image Translation Translation

An Empirical Experiment on Deep Learning Models for Predicting Traffic Data

no code implementations12 May 2021 Hyunwook Lee, Cheonbok Park, Seungmin Jin, Hyeshin Chu, Jaegul Choo, Sungahn Ko

For example, it is difficult to figure out which models provide state-of-the-art performance, as recently proposed models have often been evaluated with different datasets and experiment environments.

VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization

1 code implementation CVPR 2021 Seunghwan Choi, Sunghyun Park, Minsoo Lee, Jaegul Choo

The task of image-based virtual try-on aims to transfer a target clothing item onto the corresponding region of a person, which is commonly tackled by fitting the item to the desired body part and fusing the warped item with the person.

Virtual Try-on

RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening

3 code implementations CVPR 2021 Sungha Choi, Sanghun Jung, Huiwon Yun, Joanne Kim, Seungryong Kim, Jaegul Choo

Enhancing the generalization capability of deep neural networks to unseen domains is crucial for safety-critical applications in the real world such as autonomous driving.

Autonomous Driving Domain Generalization +1

K-Hairstyle: A Large-scale Korean Hairstyle Dataset for Virtual Hair Editing and Hairstyle Classification

no code implementations11 Feb 2021 Taewoo Kim, Chaeyeon Chung, Sunghyun Park, Gyojung Gu, Keonmin Nam, Wonzo Choe, Jaesung Lee, Jaegul Choo

In response, we introduce a novel large-scale Korean hairstyle dataset, K-hairstyle, containing 500, 000 high-resolution images.


Stego Networks: Information Hiding on Deep Neural Networks

no code implementations1 Jan 2021 Youngwoo Cho, Beomsoo Kim, Jaegul Choo

This paper considers neural networks as novel steganographic cover media, which we call stego networks, that can be used to hide one's secret messages.

Learning to Generate Questions by Recovering Answer-containing Sentences

no code implementations1 Jan 2021 Seohyun Back, Akhil Kedia, Sai Chetan Chinthakindi, Haejun Lee, Jaegul Choo

We evaluate our method against existing ones in terms of the quality of generated questions as well as the fine-tuned MRC model accuracy after training on the data synthetically generated by our method.

Ranked #4 on Question Generation on SQuAD1.1 (using extra training data)

Machine Reading Comprehension Question Answering +1

Knowledge Graph-based Question Answering with Electronic Health Records

1 code implementation19 Oct 2020 Junwoo Park, Youngwoo Cho, Haneol Lee, Jaegul Choo, Edward Choi

Question Answering (QA) is a widely-used framework for developing and evaluating an intelligent machine.

Question Answering

Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning

no code implementations ACL 2021 Cheonbok Park, Yunwon Tae, Taehee Kim, Soyoung Yang, Mohammad Azam Khan, Eunjeong Park, Jaegul Choo

To address this issue, this paper presents a novel meta-learning algorithm for unsupervised neural machine translation (UNMT) that trains the model to adapt to another domain by utilizing only a small amount of training data.

Meta-Learning Transfer Learning +2

Neural Ordinary Differential Equations for Intervention Modeling

1 code implementation16 Oct 2020 Daehoon Gwak, Gyuhyeon Sim, Michael Poli, Stefano Massaroli, Jaegul Choo, Edward Choi

By interpreting the forward dynamics of the latent representation of neural networks as an ordinary differential equation, Neural Ordinary Differential Equation (Neural ODE) emerged as an effective framework for modeling a system dynamics in the continuous time domain.

Time Series

Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equation

1 code implementation16 Oct 2020 Sunghyun Park, Kangyeol Kim, Junsoo Lee, Jaegul Choo, Joonseok Lee, Sookyung Kim, Edward Choi

Video generation models often operate under the assumption of fixed frame rates, which leads to suboptimal performance when it comes to handling flexible frame rates (e. g., increasing the frame rate of the more dynamic portion of the video as well as handling missing video frames).

Frame Video Generation

HyperTendril: Visual Analytics for User-Driven Hyperparameter Optimization of Deep Neural Networks

no code implementations4 Sep 2020 Heungseok Park, Yoonsoo Nam, Ji-Hoon Kim, Jaegul Choo

HyperTendril takes a novel approach to effectively steering hyperparameter optimization through an iterative, interactive tuning procedure that allows users to refine the search spaces and the configuration of the AutoML method based on their own insights from given results.

Hyperparameter Optimization

Towards Lightweight Lane Detection by Optimizing Spatial Embedding

1 code implementation 2020 Seokwoo Jung, Sungha Choi, Mohammad Azam Khan, Jaegul Choo

This paper addresses the problem that pixel embedding in proposal-free instance segmentation based lane detection is difficult to optimize.

Instance Segmentation Lane Detection +2

Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence

1 code implementation CVPR 2020 Junsoo Lee, Eungyeup Kim, Yunsung Lee, Dongjun Kim, Jaehyuk Chang, Jaegul Choo

However, it is difficult to prepare for a training data set that has a sufficient amount of semantically meaningful pairs of images as well as the ground truth for a colored image reflecting a given reference (e. g., coloring a sketch of an originally blue car given a reference green car).

Colorization Semantic correspondence

NeurQuRI: Neural Question Requirement Inspector for Answerability Prediction in Machine Reading Comprehension

no code implementations ICLR 2020 Seohyun Back, Sai Chetan Chinthakindi, Akhil Kedia, Haejun Lee, Jaegul Choo

Real-world question answering systems often retrieve potentially relevant documents to a given question through a keyword search, followed by a machine reading comprehension (MRC) step to find the exact answer from them.

Machine Reading Comprehension Question Answering

Cars Can't Fly up in the Sky: Improving Urban-Scene Segmentation via Height-driven Attention Networks

1 code implementation CVPR 2020 Sungha Choi, Joanne T. Kim, Jaegul Choo

This paper exploits the intrinsic features of urban-scene images and proposes a general add-on module, called height-driven attention networks (HANet), for improving semantic segmentation for urban-scene images.

Scene Segmentation

Exploring Unlabeled Faces for Novel Attribute Discovery

1 code implementation CVPR 2020 Hyojin Bahng, Sunghyo Chung, Seungjoo Yoo, Jaegul Choo

Despite remarkable success in unpaired image-to-image translation, existing systems still require a large amount of labeled images.

Image-to-Image Translation Translation

ST-GRAT: A Novel Spatio-temporal Graph Attention Network for Accurately Forecasting Dynamically Changing Road Speed

1 code implementation29 Nov 2019 Cheonbok Park, Chunggi Lee, Hyojin Bahng, Yunwon Tae, Kihwan Kim, Seungmin Jin, Sungahn Ko, Jaegul Choo

Predicting road traffic speed is a challenging task due to different types of roads, abrupt speed change and spatial dependencies between roads; it requires the modeling of dynamically changing spatial dependencies among roads and temporal patterns over long input sequences.

Graph Attention

Unpaired Image Translation via Adaptive Convolution-based Normalization

no code implementations29 Nov 2019 Wonwoong Cho, Kangyeol Kim, Eungyeup Kim, Hyunwoo J. Kim, Jaegul Choo

Disentangling content and style information of an image has played an important role in recent success in image translation.


NL2pSQL: Generating Pseudo-SQL Queries from Under-Specified Natural Language Questions

no code implementations IJCNLP 2019 Fuxiang Chen, Seung-won Hwang, Jaegul Choo, Jung-Woo Ha, Sunghun Kim

Here we describe a new NL2pSQL task to generate pSQL codes from natural language questions on under-specified database issues, NL2pSQL.


Learning De-biased Representations with Biased Representations

2 code implementations ICML 2020 Hyojin Bahng, Sanghyuk Chun, Sangdoo Yun, Jaegul Choo, Seong Joon Oh

This tactic is feasible in many scenarios where it is much easier to define a set of biased representations than to define and quantify bias.

Progressive Upsampling Audio Synthesis via Effective Adversarial Training

no code implementations25 Sep 2019 Youngwoo Cho, Minwook Chang, Gerard Jounghyun Kim, Jaegul Choo

This paper proposes a novel generative model called PUGAN, which progressively synthesizes high-quality audio in a raw waveform.

Audio Generation

ASGen: Answer-containing Sentence Generation to Pre-Train Question Generator for Scale-up Data in Question Answering

no code implementations25 Sep 2019 Akhil Kedia, Sai Chetan Chinthakindi, Seohyun Back, Haejun Lee, Jaegul Choo

We evaluate the question generation capability of our method by comparing the BLEU score with existing methods and test our method by fine-tuning the MRC model on the downstream MRC data after training on synthetic data.

Language Modelling Machine Reading Comprehension +2

SANVis: Visual Analytics for Understanding Self-Attention Networks

no code implementations13 Sep 2019 Cheonbok Park, Inyoup Na, Yongjang Jo, Sungbok Shin, Jaehyo Yoo, Bum Chul Kwon, Jian Zhao, Hyungjong Noh, Yeonsoo Lee, Jaegul Choo

Attention networks, a deep neural network architecture inspired by humans' attention mechanism, have seen significant success in image captioning, machine translation, and many other applications.

Image Captioning Machine Translation +1

What and Where to Translate: Local Mask-based Image-to-Image Translation

no code implementations9 Jun 2019 Wonwoong Cho, Seunghwan Choi, Junwoo Park, David Keetae Park, Tao Qin, Jaegul Choo

First, those methods extract style from an entire exemplar which includes noisy information, which impedes a translation model from properly extracting the intended style of the exemplar.

Image-to-Image Translation Translation

Coloring With Limited Data: Few-Shot Colorization via Memory-Augmented Networks

1 code implementation9 Jun 2019 Seungjoo Yoo, Hyojin Bahng, Sunghyo Chung, Junsoo Lee, Jaehyuk Chang, Jaegul Choo

Despite recent advancements in deep learning-based automatic colorization, they are still limited when it comes to few-shot learning.

Colorization Few-Shot Learning

Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformation

2 code implementations CVPR 2019 Wonwoong Cho, Sungha Choi, David Keetae Park, Inkyu Shin, Jaegul Choo

However, applying this approach in image translation is computationally intensive and error-prone due to the expensive time complexity and its non-trivial backpropagation.

Image-to-Image Translation Style Transfer +1

Interpreting Models by Allowing to Ask

no code implementations13 Nov 2018 Sungmin Kang, David Keetae Park, Jaehyuk Chang, Jaegul Choo

Questions convey information about the questioner, namely what one does not know.


Question-Aware Sentence Gating Networks for Question and Answering

no code implementations20 Jul 2018 Minjeong Kim, David Keetae Park, Hyungjong Noh, Yeonsoo Lee, Jaegul Choo

Machine comprehension question answering, which finds an answer to the question given a passage, involves high-level reasoning processes of understanding and tracking the relevant contents across various semantic units such as words, phrases, and sentences in a document.

Question Answering Reading Comprehension

RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records

no code implementations28 May 2018 Bum Chul Kwon, Min-Je Choi, Joanne Taery Kim, Edward Choi, Young Bin Kim, Soonwook Kwon, Jimeng Sun, Jaegul Choo

Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers.

Coloring with Words: Guiding Image Colorization Through Text-based Palette Generation

1 code implementation ECCV 2018 Hyojin Bahng, Seungjoo Yoo, Wonwoong Cho, David K. Park, Ziming Wu, Xiaojuan Ma, Jaegul Choo

This paper proposes a novel approach to generate multiple color palettes that reflect the semantics of input text and then colorize a given grayscale image according to the generated color palette.


Visual Analytics for Explainable Deep Learning

no code implementations7 Apr 2018 Jaegul Choo, Shixia Liu

Recently, deep learning has been advancing the state of the art in artificial intelligence to a new level, and humans rely on artificial intelligence techniques more than ever.

Decision Making

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

33 code implementations CVPR 2018 Yunjey Choi, Min-Je Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, Jaegul Choo

To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model.

 Ranked #1 on Image-to-Image Translation on RaFD (using extra training data)

Image-to-Image Translation Translation

End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks

1 code implementation7 Mar 2017 Min-Je Choi, Sehun Jeong, Hakjoo Oh, Jaegul Choo

Our experimental results using source codes demonstrate that our proposed model is capable of accurately detecting simple buffer overruns.

Question Answering

PixelSNE: Visualizing Fast with Just Enough Precision via Pixel-Aligned Stochastic Neighbor Embedding

1 code implementation8 Nov 2016 Minjeong Kim, Minsuk Choi, Sunwoong Lee, Jian Tang, Haesun Park, Jaegul Choo

Embedding and visualizing large-scale high-dimensional data in a two-dimensional space is an important problem since such visualization can reveal deep insights out of complex data.

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