[A17] Learning to Generate Noise for Robustness against Multiple Perturbations
Divyam Madaan, Jinwoo Shin and Sung Ju Hwang, June 2020
[paper

[A16] Federated Continual Learning with Weighted Inter-Client Transfer
Jaehong Yoon*, Wonyong Jeong*, Giwoong Lee, Eunho Yang, and Sung Ju Hwang, arXiv:2003.03196, June 2020
[paper

[A15] Federated Semi-Supervised Learning with Inter-Client Consistency
Wonyong Jeong, Jaehong Yoon, Eunho Yang, and Sung Ju Hwang, arXiv:2006.12097, June 2020
[paper

[A14] 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, arXiv:2004.02863, May 2020
[paper

[A13] Transductive Few-shot Learning with Meta-Learned Confidence
Seong Min Kye, Hae Beom Lee, Hoirin Kim, and Sung Ju Hwang, arXiv:2002.12017, June 2020
[paper

[A12] Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning
Tuan A. Nguyen*, Hyewon Jeong*, Eunho Yang, and Sung Ju Hwang, arXiv:2006.12777, June 2020
(*: equal contribution) 
[paper

[A11] Stochastic Subset Selection
Tuan A. Nguyen*, Bruno Andreis*, Juho Lee, Eunho Yang, and Sung Ju Hwang, June 2020
(*: equal contribution) 
[paper

[A10] Adversarial Self-Supervised Contrastive Learning
Minseon Kim, Jihoon Tack, and Sung Ju Hwang, arXiv:2006.07589, June 2020
[paper

[A9] Rapid Structural Pruning of Neural Networks with Set-based Task Adaptive Meta-Pruning
Minyoung Song, Jaehong Yoon, and Sung Ju Hwang, June 2020
[paper

[A8] Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
Jinheon Baek, Dong Bok Lee, and Sung Ju Hwang, arXiv:2006.06648, June 2020
[paper] 

[A7] MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures
Jeongun Ryu, Jaewoong Shin, Hae Beom Lee, and Sung Ju Hwang, arXiv:2006.07540, June 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

[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​]

[W10] Adversarial Neural Pruning
Divyam Madaan, Jinwoo Shin and Sung Ju Hwang, NeurIPS Workshop on Safety and Robustness in Decision Making, NeurIPS 2019
[paper]

[W9] Uncertainty-Aware Deep Temporal Asymmetric Multi-task Learning
Hyewon Jeong, Tuan Anh Nguyen, Eunho Yang and Sung Ju Hwang, Women in Machine Learning Workshop, NeurIPS 2019

[A6] Semi-Relaxed Quantization with DropBits: Training Low-Bit Neural Networks via Bit-wise Regularization
Jihun Yun, Jung Hyun Lee, Sung Ju Hwang and Eunho Yang, arXiv:1911.12990, Nov 2019

[paper] 

[A5] Learning to Disentangle Robust and Vulnerable Features for Adversarial Detection
Byunggill Joe, Sung Ju Hwang and Insik Shin, arXiv:1909.04311, Sep 2019
[paper] 

[W8]  Interactive Attention Learning for Action Recognition
Jay Heo, Junhyeon Park, Hyewon Jeong, Wuhyun Shin, Kwang Joon Kim, and Sung Ju Hwang, 
ICCV Workshop on Interpreting and Explaining Visual Artificial Intelligence Models, ICCV 2019

[A4] Reliable Estimation of Individual Treatment Effect with Causal Information Bottleneck

Sungyub Kim, Yongsu Baek, Sung Ju Hwang and Eunho Yang, arXiv:1906.03118, Jun 2019
[
paper]

[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​]

[A3] Learning to Separate Domains in Generalized Zero-Shot and Open Set Learning: a probabilistic perspective
Hanze Dong, Yanwei Fu, Leonid Sigal, Sung Ju Hwang, Yu-Gang Jiang and Xiangyang Xue, arXiv:1810.07368, Nov 2018
[paper]

[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​]

[A2] Adaptive Network Sparsification with Dependent Beta-Bernoulli Dropout
Juho Lee, Saehoon Kim, Haebeom Lee, Jaehong Yoon, Eunho Yang, and Sung Ju Hwang, arXiv:1805.10896, May 2018
[paper]

[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]

[W6] A Metric Learning Approach for Multi-View Object Recognition and Zero-shot Pose Estimation
Alina Kuznetsova, Sung Ju Hwang, Bodo Rosenhahn and Leonid Sigal, ICCV Workshop on Object Understanding for Interaction, ICCV 2015
[paper]

[W5] Interactive Semantics for Knowledge Transfer
Jonghyun Choi, Sung Ju Hwang, Leonid Sigal and Larry S. Davis, ICML Active Learning Workshop,
ICML 2015
[paper]

[W4] A Unified Semantic Embedding: Relating Taxonomies and Attributes
Sung Ju Hwang and Leonid Sigal, AAAI Spring Symposium on Knowledge Representation and Reasoning ,
KRR 2015

[paper]

[A1] Hierarchical Maximum-Margin Clustering
Guang-Tong Zhou, Sung Ju Hwang, Mark Schmidt, Leonid Sigal and Greg Mori, arXiv:1502.01827, Feb 2015
[paper]

[W3] A Unified Semantic Embedding: Relating Taxonomies and Attributes

Sung Ju Hwang and Leonid Sigal, NIPS Workshop on Learning Semantics, NIPS 2014
[paper]

[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]

[J2] Learning the Relative Importance of Objects from Tagged Images for Retrieval and Cross-Modal Search 
Sung Ju Hwang and Kristen Grauman, International Journal of Computer Vision (IJCV), November 2012
[paper] [codes&data]

[J1] Reading Between the Lines: Object Localization Using Implicit Cues from Image Tags
Sung Ju Hwang and Kristen Grauman, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), June 2012
[paper]

[W2] Semantic Kernel Forests from Multiple Taxonomies
Sung Ju Hwang, Fei Sha and Kristen Grauman, Big Data Meets Computer Vision: International Workshop on Large Scale Visual Recognition and Retrieval (BigVision), NIPS 2012 (oral presentation)
[paper]

[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]

[W1] Sharing Features Between Visual Tasks at Different Levels of Granularity
Sung Ju Hwang, Fei Sha and Kristen Grauman, Fine-Grained Visual Categorization Workshop (FGVC),
CVPR 2011
[paper]

[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]

LINKS