Our group actively publishes in the fields of machine learning, computer vision, and interdisciplinary data science. Below are a list of recent and selected papers. A mark * denotes the author to be a VITA student or Dr. Wang's mentee. An up-to-date full paper list can be found here.
Journal Paper
- J. Yan, Y. Zhong, Y. Fang, Z. Wang, and K. Ma
“Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition”
International Journal of Computer Vision (IJCV), 2021. [Paper] [Code]
- S. Yang*, Z. Wang, and J. Liu
“Shape-Matching GAN++: Scale Controllable Dynamic Artistic Text Style Transfer”
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021. [Paper]
- Z. Wu*, H. Wang*, Z. Wang, H. Jin, and Z. Wang
“Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset”
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020. [Paper] [Code]
- Y. Jiang*, X. Gong*, D. Liu, Y. Cheng, C. Fang, X. Shen, J. Yang, P. Zhou, and Z. Wang
“EnlightenGAN: Deep Light Enhancement without Paired Supervision”
IEEE Transactions on Image Processing (TIP), 2020. [Paper] [Code]
- M. Karimi, D. Wu, Z. Wang, and Y. Shen,
“Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts”
Journal of Chemical Information and Modeling (JCIM), 2020. [Paper] [Code]
- S. Li, W. Ren, F. Wang, I. Araujo*, E. K. Tokuda*, R. Hirata, R. Cesar, Z. Wang, and X. Cao,
“A Comprehensive Benchmark Analysis of Single Image Deraining: Current Challenges and Future Perspectives”
International Journal of Computer Vision (IJCV), 2020. [Paper]
- Y. Yuan*, W. Yang, W. Ren, J Liu, W. J. Scheirer, and Z. Wang, et. al.
“Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study”
IEEE Transactions on Image Processing (TIP), 2020. [Paper]
- M. Karimi, D. Wu, Z. Wang and Y. Shen
“DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks”
Oxford Bioinformatics, 2019. [Paper] [Code]
- R. G. VidalMata, ... Y. Yuan*, J. Wu*, Z. Wang, ... et. al.
“Bridging the Gap Between Computational Photography and Visual Recognition”
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020. [Paper][Code]
- Y. Wang*, J. Shen*, T. Hu*, T. Nguyen, R. Baraniuk, Z. Wang, and Y. Lin
“Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference”
IEEE Journal of Selected Topics in Signal Processing (JSTSP), 2020. [Paper]
- B. Li*, W. Ren, D. Fu, D. Tao, D. Feng, W. Zeng, and Z. Wang
“Benchmarking Single Image Dehazing and Beyond”
IEEE Transactions on Image Processing (TIP), vol. 28, no. 1, pp. 492-505, 2019. [Paper] [Project Page]
Conference Paper
- Y. You*, T. Chen*, Y. Shen, and Z. Wang
“Graph Contrastive Learning Automated”
International Conference on Machine Learning (ICML), 2021. [Paper] [Code]
- M. Zhu*, T. Chen*, and Z. Wang
“Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm”
International Conference on Machine Learning (ICML), 2021. [Paper] [Code]
- T. Chen*, Y. Sui, X. Chen*, A. Zhang, and Z. Wang
“A Unified Lottery Ticket Hypothesis for Graph Neural Networks”
International Conference on Machine Learning (ICML), 2021. [Paper][Code]
- Z. Jiang*, T. Chen*, B. Mortazavi, and Z. Wang
“Self-Damaging Contrastive Learning”
International Conference on Machine Learning (ICML), 2021. [Paper] [Code]
- Z. Zhang*, X. Chen*, T. Chen*, and Z. Wang
“Efficient Lottery Ticket Finding: Less Data is More”
International Conference on Machine Learning (ICML), 2021. [Paper] [Code]
- X. Chen*, Y. Cheng, S. Wang, Z. Gan, Z. Wang, and J. Liu
“EarlyBERT: Efficient BERT Training via Early-Bird Lottery Tickets”
Annual Meeting of the Association for Computational Linguistics (ACL), 2021. [Paper][Code]
- T. Chen*, J. Frankle, S. Chang, S. Liu, Y. Zhang, M. Carbin, and Z. Wang
“The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models”
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. [Paper][Code]
- Z. Wang, H. Wang*, T. Chen*, Z. Wang, and K. Ma
“Troubleshooting Blind Image Quality Models in the Wild”
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. [Paper] [Code]
- P. Cao, Z. Wang, and K. Ma
“Debiased Subjective Assessment of Real-World Image Enhancement”
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. [Paper] [Code]
- H. Ma, T. Chen*, T. Hu*, C. You, X. Xie, and Z. Wang
“Undistillable: Making A Nasty Teacher That CANNOT Teach Students”
International Conference on Learning Representations (ICLR), 2021. (Spotlight Oral) [Paper][Code]
- T. Chen*, Z. Zhang*, S. Liu, S. Chang, and Z. Wang
“Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning”
International Conference on Learning Representations (ICLR), 2021. [Paper] [Code]
- T. Chen*, Z. Zhang*, S. Liu, S. Chang, and Z. Wang
“Robust Overfitting May be Mitigated by Properly Learned Smoothening”
International Conference on Learning Representations (ICLR), 2021. [Paper] [Code]
- W. Chen*, X. Gong*, and Z. Wang
“Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective”
International Conference on Learning Representations (ICLR), 2021. [Paper] [Code]
- W. Chen*, Z. Yu, S. Mello, S. Liu, J. Alvarez, Z. Wang, and A. Anandkumar
“Contrastive Syn-to-Real Generalization”
International Conference on Learning Representations (ICLR), 2021. [Paper] [Code]
- T. Meng, X. Chen*, Y. Jiang*, and Z. Wang
“A Design Space Study for LISTA and Beyond”
International Conference on Learning Representations (ICLR), 2021. [Paper] [Code]
- J. Shen*, X. Chen*, H. Heaton, T. Chen*, J. Liu, W. Yin, and Z. Wang
“Learning A Minimax Optimizer: A Pilot Study”
International Conference on Learning Representations (ICLR), 2021. [Paper] [Code]
- J. Shen*, H. Wang*, S. Gui, J. Tan, Z. Wang, and J. Liu
“UMEC: Unified Model and Embedding Compression for Efficient Recommendation Systems”
International Conference on Learning Representations (ICLR), 2021. [Paper] [Code]
- J. Hong, H. Wang*, Z. Wang, and J. Zhou
“Learning Model-Based Privacy Protection under Budget Constraints”
AAAI Conference on Artificial Intelligence (AAAI), 2021.[Paper] [Code]
- T. Chen*, W. Zhang, J. Zhou, S. Chang, S. Liu, L. Amini, and Z. Wang
“Training Stronger Baselines for Learning to Optimize”
Advances in Neural Information Processing Systems (NeurIPS), 2020. (Spotlight Oral) [Paper] [Code]
- H. Wang*, T. Chen*, S. Gui, T. Hu*, J. Liu, and Z. Wang
“Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free”
Advances in Neural Information Processing Systems (NeurIPS), 2020. [Paper] [Code]
- T. Chen*, J. Frankle, S. Chang, S. Liu, Y. Zhang, Z. Wang, and M. Carbin
“The Lottery Ticket Hypothesis for Pre-trained BERT Networks”
Advances in Neural Information Processing Systems (NeurIPS), 2020. [Paper] [Code]
- X. Chen*, Z. Wang, S. Tang, and K. Muandet
“MATE: Plugging in Model Awareness to Task Embedding for Meta Learning”
Advances in Neural Information Processing Systems (NeurIPS), 2020. [Paper] [Code]
- Z. Jiang*, T. Chen*, T. Chen, and Z. Wang
“Robust Pre-Training by Adversarial Contrastive Learning”
Advances in Neural Information Processing Systems (NeurIPS), 2020. [Paper] [Code]
- Y. You*, T. Chen*, Y. Sui, T. Chen, Z. Wang, and Y. Shen
“Graph Contrastive Learning with Augmentations”
Advances in Neural Information Processing Systems (NeurIPS), 2020. [Paper] [Code]
- H. You, X. Chen*, Y. Zhang, C. Li, S. Li, Z. Liu, Z. Wang, and Y. Lin
“ShiftAddNet: A Hardware-Inspired Deep Network”
Advances in Neural Information Processing Systems (NeurIPS), 2020. [Paper] [Code]
- Y. Fu, H. You, Y. Zhao, Y. Wang, C. Li, K. Gopalakrishnan, Z. Wang, and Y. Lin
“FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training”
Advances in Neural Information Processing Systems (NeurIPS), 2020. [Paper] [Code]
- Z. Wu*, D. Hoang*, S. Lin, Y. Xie, L. Chen, Y. Lin, Z. Wang, and W. Fan
“MM-Hand: 3D-Aware Multi-Modal Guided Hand Generation for 3D Hand Pose Synthesis”
ACM International Conference on Multimedia (ACM MM), 2020. [Paper] [Code]
- H. Wang*, S. Gui, H. Yang, J. Liu, and Z. Wang
“GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework”
European Conference on Computer Vision (ECCV), 2020. (Spotlight Oral) [Paper][Code]
- S. Yang*, Z. Wang, J. Liu, and Z. Guo
“Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches”
European Conference on Computer Vision (ECCV), 2020. [Paper] [Code]
- C. Li, T. Chen*, H. You, Z. Wang, and Y Lin
“HALO: Hardware-Aware Learning to Optimize”
European Conference on Computer Vision (ECCV), 2020. [Paper] [Code]
- Z. Huo, A. PakBin, X. Chen*, N. Hurley, Y. Yuan*, X. Qian, Z. Wang, S. Huang, and B. Mortazavi
“Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery”
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. [Paper] [Code]
- W. Chen*, Z. Yu, Z. Wang, and A. Anandkumar
“Automated Synthetic-to-Real Generalization”
International Conference on Machine Learning (ICML), 2020. [Paper] [Code]
- X. Chen*, W. Chen*, T. Chen*, Y. Yuan*, C. Gong, K. Chen, and Z. Wang
“Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training”
International Conference on Machine Learning (ICML), 2020. [Paper] [Code]
- Y. You*, T. Chen*, Z. Wang, and Y. Shen
“When Does Self-Supervision Help Graph Convolutional Networks?”
International Conference on Machine Learning (ICML), 2020. [Paper] [Code]
- R. Oftadeh, J. Shen*, Z. Wang, and D. Shell
“Eliminating the Invariance on the Loss Landscape of Linear Autoencoders”
International Conference on Machine Learning (ICML), 2020. [Paper] [Code]
- Y. Fu, W. Chen*, H. Wang*, H. Li, Y. Lin, and Z. Wang
“AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks”
International Conference on Machine Learning (ICML), 2020. [Paper] [Code]
- R. Ardywibowo, S. Boluki, X. Gong*, Z. Wang, and X. Qian
“NADS: Neural Architecture Distribution Search for Uncertainty Awareness”
International Conference on Machine Learning (ICML), 2020. [Paper] [Code]
- Y. Zhao, X. Chen*, Y. Wang, C. Li, Y. Xie, Z. Wang, and Y. Lin
“SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation”
IEEE/ACM International Symposium on Computer Architecture (ISCA), 2020. [Paper] [Code]
- T. Chen*, S. Liu, S. Chang, Y. Cheng, L. Amini, and Z. Wang
“Adversarial Robustness: From Self-Supervised Pretraining to Fine-Tuning”
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [Paper] [Code]
- Z. Jiang*, B. Liu, S. Schulter, Z. Wang, and M. Chandraker
“Peek-a-boo: Occlusion Reasoning in Indoor Scenes with Plane Representations”
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. (Oral) [Paper] [Code]
- Y. You*, T. Chen*, Z. Wang, and Y. Shen
“L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks”
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [Paper] [Code]
- T. Hu*, T. Chen*, H. Wang*, and Z. Wang
"Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference"
International Conference on Learning Representations (ICLR), 2020. [Paper] [Code]
- W. Chen*, X. Gong*, X. Liu, Q. Zhang, Y. Li and Z. Wang
"FasterSeg: Searching for Faster Real-time Semantic Segmentation"
International Conference on Learning Representations (ICLR), 2020. [Paper] [Code]
- H. Wang*, T. Chen*, Z. Wang, and K. Ma
"I am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively"
International Conference on Learning Representations (ICLR), 2020. [Paper] [Code]
- H. You, C. Li, P. Xu, Y. Fu, Y. Wang, X. Chen*, R. Baraniuk, Z. Wang, and Y. Lin
“Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks"
International Conference on Learning Representations (ICLR), 2020. (Spotlight Oral) [Paper] [Code]
- J. Shen*, Y. Wang*, P. Xu, Y. Fu, Z. Wang, and Y. Lin
“Fractional Skipping: Toward Finer-Grained Dynamic Inference”
AAAI Conference on Artificial Intelligence (AAAI), 2020. [Paper] [Code]
- S. Mohseni*, M. Pitale, J. Yadawa, and Z. Wang
“Self-Supervised Learning for Generalizable Out-of-Distribution Detection”
AAAI Conference on Artificial Intelligence (AAAI), 2020. [Paper] [Code]
- Z. Jiang*, Y. Wang*, X. Chen*, P. Xu, Y. Zhao, Y. Lin, and Z. Wang
“E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings”
Advances in Neural Information Processing Systems (NeurIPS), 2019. [Paper] [Code]
- S. Gui, H. Wang*, H. Yang, C. Yu, Z. Wang, and J. Liu
“Model Compression with Adversarial Robustness: A Unified Optimization Framework”
Advances in Neural Information Processing Systems (NeurIPS), 2019. [Paper] [Code]
- Y. Cao, T. Chen*, Z. Wang, and Y. Shen
“Learning to Optimize in Swarms”
Advances in Neural Information Processing Systems (NeurIPS), 2019. [Paper] [Code]
- X. Jia, S. Wang*, X. Liang, A. Balagopal, D. Nguyen, M. Yang, Z. Wang, X. Qian, X. Ji, and S. Jiang
“Cone-Beam Computed Tomography (CBCT) Segmentation by Adversarial Learning Domain Adaptation”
Medical Image Computing and Computer Assisted Interventions (MICCAI), 2019 [Paper] [Code]
- R. Ardywibowo, G. Zhao, Z. Wang, B. Mortazavi, S. Huang, and X. Qian,
“Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models”
International Conference on Artificial Intelligence and Statistics (AISTATS), 2019 [Paper] [Code]
- S. Yang*, Z. Wang, Z Wang, N. Xu, J. Liu, and Z. Guo
“Controllable Artistic Text Style Transfer via Shape-Matching GAN”
IEEE International Conference on Computer Vision (ICCV), 2019. (Oral) [Paper] [Code]
- Z. Wu*, K. Suresh, P. Narayanan, H. Xu, H. Kwon, and Z. Wang
“Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach”
IEEE International Conference on Computer Vision (ICCV), 2019. [Paper] [Code]
- X. Gong*, S. Chang, Y. Jiang*, and Z. Wang
“AutoGAN: Neural Architecture Search for Generative Adversarial Networks”
IEEE International Conference on Computer Vision (ICCV), 2019. [Paper] [Code]
- T. Chen*, S. Ding, J. Xie, Y. Yuan*, W. Chen*, Y. Yang, Z. Ren, and Z. Wang
“ABD-Net: Attentive but Diverse Person Re-Identification”
IEEE International Conference on Computer Vision (ICCV), 2019. [Paper] [Code]
- O. Kupyn, T. Martyniuk, J. Wu*, and Z. Wang
“DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better”
IEEE International Conference on Computer Vision (ICCV), 2019. [Paper] [Code]
- E. Ryu, J. Liu, S. Wang*, X. Chen*, Z. Wang, and W. Yin
“Plug-and-Play Methods Provably Converge with Properly Trained Denoisers”
International Conference on Machine Learning (ICML), 2019. [Paper] [Code]
- W. Chen*, Z. Jiang*, Z. Wang, K. Cui, and X. Qian
“Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-high Resolution Images”
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. (Oral) [Paper] [Code]
- S. Li, I. B. Araujo*, W. Ren, Z. Wang, E. K. Tokuda*, R. Hirata, R. Cesar, J. Zhang, X. Guo, and X. Cao
“Single Image Deraining: A Comprehensive Benchmark Analysis”
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. [Paper] [Code]
- J. Liu, X. Chen*, Z. Wang, and W. Yin
“ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA”
International Conference on Learning Representations (ICLR), 2019. [Paper] [Code]
- X. Chen*, J. Liu, Z. Wang, and W. Yin
“Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds”
Advances in Neural Information Processing Systems (NeurIPS), 2018. (Spotlight Oral) [Paper] [Code]
- N. Bansal*, X. Chen*, and Z. Wang
“Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?”
Advances in Neural Information Processing Systems (NeurIPS), 2018. [Paper] [Code]
- Z. Wu*, Z. Wang, Z. Wang, and H. Jin
“Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study”
European Conference on Computer Vision (ECCV), 2018. [Paper] [Code]
- M. Sun, I. Baytas, L. Zhan, Z. Wang, and J. Zhou
“Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases”
ACM Conference on Knowledge Discovery and Data Mining (KDD), 2018. [Paper] [Code]
- J. Wu*, Y. Wang*, Z. Wu*, Z. Wang, A. Veeraraghavan, and Y. Lin
“Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions”
International Conference on Machine Learning (ICML), 2018. [Paper] [Code]
