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.
1 code implementation • 27 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.
no code implementations • 12 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.
no code implementations • 21 Dec 2021 • Kangyeol Kim, Sunghyun Park, Junsoo Lee, Joonseok Lee, Sookyung Kim, Jaegul Choo, Edward Choi
In order to perform unconditional video generation, we must learn the distribution of the real-world videos.
no code implementations • 7 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.
no code implementations • 16 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.
no code implementations • 15 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.
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.
1 code implementation • 22 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.
no code implementations • 18 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.
no code implementations • 29 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.
no code implementations • 29 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.
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.
no code implementations • 29 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.
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.
no code implementations • 17 Aug 2021 • Hojoon Lee, Dongyoon Hwang, Sunghwan Hong, Changyeon Kim, Seungryong Kim, Jaegul Choo
Successful sequential recommendation systems rely on accurately capturing the user's short-term and long-term interest.
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.
Ranked #5 on
Anomaly Detection
on Fishyscapes L&F
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.
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.
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.
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.
no code implementations • 12 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.
no code implementations • NAACL 2021 • Kyeongpil Kang, Kyohoon Jin, Soyoung Yang, Sujin Jang, Jaegul Choo, Youngbin Kim
Understanding voluminous historical records provides clues on the past in various aspects, such as social and political issues and even natural science facts.
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.
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.
no code implementations • 11 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.
no code implementations • 1 Jan 2021 • Junsoo Lee, Hojoon Lee, Inkyu Shin, Jaekyoung Bae, In So Kweon, Jaegul Choo
Learning visual representations using large-scale unlabelled images is a holy grail for most of computer vision tasks.
no code implementations • 1 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.
no code implementations • 1 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)
no code implementations • 26 Nov 2020 • Jeonghoon Park, Kyungmin Jo, Daehoon Gwak, Jimin Hong, Jaegul Choo, Edward Choi
We evaluate the out-of-distribution (OOD) detection performance of self-supervised learning (SSL) techniques with a new evaluation framework.
1 code implementation • 19 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.
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.
1 code implementation • 16 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.
1 code implementation • 16 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).
no code implementations • 4 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.
1 code implementation • arXiv.org 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.
Ranked #9 on
Lane Detection
on TuSimple
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).
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.
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.
Ranked #10 on
Semantic Segmentation
on Cityscapes test
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.
1 code implementation • 29 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.
no code implementations • 29 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.
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.
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.
no code implementations • 25 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.
no code implementations • 25 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.
no code implementations • 13 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.
no code implementations • 9 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.
1 code implementation • 9 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.
1 code implementation • ICLR 2019 • Wonwoong Cho, Seunghwan Choi, Junwoo Park, David Keetae Park, Tao Qin, Jaegul Choo
Recently, image-to-image translation has seen a significant success.
no code implementations • 11 Feb 2019 • Sanghyeon Na, Seungjoo Yoo, Jaegul Choo
First, we use a content representation from the source domain conditioned on a style representation from the target domain.
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.
no code implementations • 13 Nov 2018 • Sungmin Kang, David Keetae Park, Jaehyuk Chang, Jaegul Choo
Questions convey information about the questioner, namely what one does not know.
no code implementations • EMNLP 2018 • Seohyun Back, Seunghak Yu, Sathish Reddy Indurthi, Jihie Kim, Jaegul Choo
Machine reading comprehension helps machines learn to utilize most of the human knowledge written in the form of text.
Ranked #11 on
Question Answering
on TriviaQA
no code implementations • 27 Sep 2018 • Egil Martinsson, Adrian Kim, Jaesung Huh, Jaegul Choo, Jung-Woo Ha
Predicting the time to the next event is an important task in various domains.
no code implementations • 20 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.
no code implementations • 28 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.
no code implementations • 7 May 2018 • David Keetae Park, Seungjoo Yoo, Hyojin Bahng, Jaegul Choo, Noseong Park
Recently, generative adversarial networks (GANs) have shown promising performance in generating realistic images.
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.
no code implementations • 7 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.
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)
no code implementations • 26 Jul 2017 • Noseong Park, Ankesh Anand, Joel Ruben Antony Moniz, Kookjin Lee, Tanmoy Chakraborty, Jaegul Choo, Hongkyu Park, Young-Min Kim
MMGAN finds two manifolds representing the vector representations of real and fake images.
1 code implementation • 7 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.
1 code implementation • 8 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.