[C46] Adversarial Self-Supervised Contrastive Learning
Minseon Kim, Jihoon Tack, and Sung Ju Hwang, NeurIPS 2020
[paper] 

[C45] Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
Jinheon Baek, Dong Bok Lee, and Sung Ju Hwang, NeurIPS 2020
[paper] 

[C44] MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures
Jeongun Ryu, Jaewoong Shin, Hae Beom Lee, and Sung Ju Hwang, NeurIPS 2020 (spotlight)
[paper] 

[C43] Time-Reversal Symmetric ODE Network
In Huh, Eunho Yang, Sung Ju Hwang, and Jinwoo Shin, NeurIPS 2020
[paper] 

[C42] Bootstrapping Neural Processes
Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, and Yee Whye Teh, NeurIPS 2020
[paper] 

[C41] Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning
Youngsung Kim, Jinwoo Shin, Eunho Yang and Sung Ju Hwang, NeurIPS 2020
[paper] 

[C40] Neural Complexity Measures
Yoonho Lee, Juho Lee, Sung Ju Hwang, Eunho Yang, and Seungjin Choi, NeurIPS 2020
[paper] 

[C39] Attribution Preservation in Network Compression for Reliable Network Interpretation
Geondo Park, June Yong Yang, Sung Ju Hwang, and Eunho Yang, NeurIPS 2020
[paper] 

[C38] Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning
Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang, Sung Ju Hwang, and Jinwoo Shin, NeurIPS 2020
[paper] 

[C37] Neural Mask Generator: Learning to Generate Adaptive Word Maskings for Language Model Adaptation
Minki Kang, Moonsu Han, Sung Ju Hwang, EMNLP 2020 (long paper)
[paper] 

[C36] Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length Pairs
Seong Min Kye, Youngmoon Jung, Hae Beom Lee, Sung Ju Hwang, and Hoirin Kim, INTERSPEECH 2020
[paper] 

[C35]  Adversarial Neural Pruning with Latent Vulnerability Suppression 
Divyam Madaan, Jinwoo Shin and Sung Ju Hwang, ICML 2020
[paper]

[C34] Cost-effective Interactive Attention Learning with Neural Attention Process
Jay Heo, Junhyeon Park, Hyewon Jeong, Kwang Joon Kim, Juho Lee, Eunho Yang and Sung Ju Hwang, ICML 2020

[paper]

[C33] Meta Variance Transfer: Learning to Augment from the Others
Seong Jin Park, Seungju Han, Ji-won Baek, Insoo Kim, Juhwan Song, Hae Beom Lee,  Jae-Joon Han and Sung Ju Hwang, ICML 2020

[paper]

[C32] Self-supervised Label Augmentation via Input Transformations
Hankook Lee, Sung Ju Hwang and Jinwoo Shin, ICML 2020
[paper]

[C31] Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs
Dong Bok Lee*, Seanie Lee*, Woo Tae Jeong, Donghwan Kim and Sung Ju Hwang,  ACL 2020 (long paper)

​(*: equal contribution)
[paper]

[C30] Segmenting 2K-Videos at 36.5 FPS with 24.3 GFLOPs: Accurate and Lightweight Realtime Semantic Segmentation Networks
Dokwan Oh, Daehyun Ji, Cheolhun Jang, Yoonsuk Hyun, Hong S. Bae and Sung Ju Hwang, ICRA 2020 
[paper]

​

[C29] Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks
Hae Beom Lee*, Hayeon Lee*, Donghyun Na*, Saehoon Kim, Minseop Park, Eunho Yang and Sung Ju Hwang, ICLR 2020 (oral presentation)(*: equal contribution) 
[paper] [codes] [bibtex​]

[C28] Meta Dropout: Learning to Perturb Latent Features for Generalization
Hae Beom Lee, Taewook Nam, Eunho Yang and Sung Ju Hwang, ICLR 2020
[paper] [codes] [bibtex​]

[C27] Scalable and Order-robust Continual Learning with Additive Parameter Decomposition
Jaehong Yoon, Saehoon Kim, Eunho Yang and Sung Ju Hwang, ICLR 2020
[paper] [codes] [bibtex​]

[C26]  Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks
Joonyoung Yi, Juhyuk Lee, Sung Ju Hwang and Eunho Yang, ICLR 2020
[paper] [bibtex​]

[C25]  Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare
Ingyo Chung, Saehoon Kim, Juho Lee, Sung Ju Hwang and Eunho Yang, AAAI 2020
[paper] [codes] [bibtex​]

[C24]  Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data
Moonsu Han*, Minki Kang*, Hyunwoo Jung, Sung Ju Hwang, ACL 2019 (long paper) (oral presentation) 
(*: equal contribution)
[paper] [codes] [bibtex​]

[C23] Learning What and Where to Transfer
Yunhun Jang, Hankook Lee, Sung Ju Hwang, and Jinwoo Shin, ICML 2019
[paper] [codes] [bibtex​]

[C22] Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss
Sangil Jung, Changyong Son, Seohyung Lee, Jinwoo Son, Jae-Joon Han, Youngjun Kwak, Sung Ju Hwang and Changkyu Choi, CVPR 2019 (oral presentation)
[paper] [bibtex​]

[C21] Learning to Propagate Labels: Transductive Propagation Networks for Few-shot Learning
Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang and Yi Yang, ICLR 2019
[paper] [codes] [bibtex​]

[C20] DropMax: Adaptive Variational Softmax
Haebeom Lee, Juho Lee, Saehoon Kim, Eunho Yang and Sung Ju Hwang, NeurIPS 2018
[paper] [codes] [bibtex​]

[C19] Uncertainty-Aware Attention for Reliable Interpretation and Prediction
Jay Heo*, Haebeom Lee*, Saehoon Kim, Juho Lee, Kwangjun Kim, Eunho Yang, and Sung Ju Hwang, NeurIPS 2018 (*: equal contribution)
[paper] [codes] [bibtex​]

[C18] Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding
Hajin Shim, Sung Ju Hwang and Eunho Yang, NeurIPS 2018
[paper] [codes] [bibtex​]

[C17] Deep Asymmetric Multi-task Feature Learning
Haebeom Lee, Eunho Yang and Sung Ju Hwang, ICML 2018
[paper] [codes] [bibtex]

[C16] Lifelong Learning with Dynamically Expandable Networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang, ICLR 2018

[paper] [codes] [bibtex]

[C15] SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization
Juyong Kim, Yookoon Park, Gunhee Kim and Sung Ju Hwang, ICML 2017
[paper] [codes] [bibtex]

[C14] Combined Group and Exclusive Sparsity for Deep Neural Networks
Jaehong Yoon and Sung Ju Hwang, ICML 2017
[paper] [codes] [bibtex]

[C13] Taxonomy-Regularized Semantic Deep Convolutional Neural Networks
Wonjoon Goo, Juyong Kim, Gunhee Kim and Sung Ju Hwang, ECCV 2016

[paper] [codes] [bibtex]

[C12] Asymmetric Multi-task Learning Based on Task Relatedness and Loss
Giwoong Lee, Eunho Yang and Sung Ju Hwang, ICML 2016
[paper] [codes] [bibtex]

[C11] Knowledge Transfer with Interactive Learning of Semantic Relationships
Jonghyun Choi, Sung Ju Hwang, Leonid Sigal, and Larry S. Davis, AAAI 2016 (oral presentation)
[paper] [bibtex]

[C10] Exploiting View-Specific Appearance Similarities Across Classes for Zero-shot Pose Prediction: A Metric Learning Approach
Alina Kuznetsova, Sung Ju Hwang, Bodo Rosenhahn, and Leonid Sigal, AAAI 2016
[paper] [bibtex]

[C9] Expanding Object Detector’s Horizon: Incremental Learning Framework for Object Detection in Videos
Alina Kuznetsova, Sung Ju Hwang, Bodo Rosenhahn, and Leonid Sigal, CVPR 2015
[paper] [bibtex]

[C8] A Unified Semantic Embedding: Relating Taxonomies with Attributes
Sung Ju Hwang and Leonid Sigal, NIPS 2014
[paper] [bibtex]

[C7] Analogy-preserving Semantic Embedding for Visual Object Categorization
Sung Ju Hwang, Kristen Grauman and Fei Sha, ICML 2013
[paper] [bibtex]

[C6] Semantic Kernel Forests from Multiple Taxonomies
Sung Ju Hwang, Kristen Grauman and Fei Sha, NIPS 2012
[paper] [bibtex]

[C5] Context-Based Automatic Local Image Enhancement

Sung Ju Hwang, Ashish Kapoor, and Sing Bing Kang, ECCV 2012

[paper] [bibtex]

[C4] Learning a Tree of Metrics with Disjoint Visual Features
Sung Ju Hwang, Kristen Grauman, and Fei Sha, NIPS 2011
[paper] [codes]​ [bibtex]

​[C3] Sharing Features Between Objects and Their Attributes
Sung Ju Hwang, Fei Sha and Kristen Grauman, CVPR 2011
[paper] [bibtex]

[C2] Accounting for the Relative Importance of Objects in Image Retrieval

Sung Ju Hwang and Kristen Grauman, BMVC 2010 (oral presentation)
[paper] [codes&data] [bibtex]

[C1] Reading Between The Lines: Object Localization Using Implicit Cues from Image Tags
Sung Ju Hwang and Kristen Grauman, CVPR 2010 (oral presentation)
[paper] [bibtex]