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ORCIDYogesh Balaji
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2020 – today
- 2024
[c18]Yu Zeng, Vishal M. Patel, Haochen Wang, Xun Huang, Ting-Chun Wang, Ming-Yu Liu, Yogesh Balaji:
JeDi: Joint-Image Diffusion Models for Finetuning-Free Personalized Text-to-Image Generation. CVPR 2024: 6786-6795- [i19]Yu Zeng, Vishal M. Patel, Haochen Wang, Xun Huang, Ting-Chun Wang, Ming-Yu Liu, Yogesh Balaji:
JeDi: Joint-Image Diffusion Models for Finetuning-Free Personalized Text-to-Image Generation. CoRR abs/2407.06187 (2024) - [i18]Zhendong Wang, Zhaoshuo Li, Ajay Mandlekar, Zhenjia Xu, Jiaojiao Fan, Yashraj S. Narang, Linxi Fan, Yuke Zhu, Yogesh Balaji, Mingyuan Zhou, Ming-Yu Liu, Yu Zeng:
One-Step Diffusion Policy: Fast Visuomotor Policies via Diffusion Distillation. CoRR abs/2410.21257 (2024) - 2023
- [c17]Songwei Ge, Seungjun Nah, Guilin Liu, Tyler Poon, Andrew Tao, Bryan Catanzaro, David Jacobs, Jia-Bin Huang, Ming-Yu Liu, Yogesh Balaji:
Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models. ICCV 2023: 22873-22884 - [i17]Songwei Ge, Seungjun Nah, Guilin Liu, Tyler Poon, Andrew Tao, Bryan Catanzaro, David Jacobs, Jia-Bin Huang, Ming-Yu Liu, Yogesh Balaji:
Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models. CoRR abs/2305.10474 (2023) - 2022
- [c16]Mazda Moayeri, Phillip Pope, Yogesh Balaji, Soheil Feizi:
A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes. CVPR 2022: 19065-19075 - [i16]Mazda Moayeri, Phillip Pope, Yogesh Balaji, Soheil Feizi:
A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes. CoRR abs/2201.10766 (2022) - [i15]Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, Tero Karras, Ming-Yu Liu:
eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers. CoRR abs/2211.01324 (2022) - 2021
- [b1]Yogesh Balaji:
Robust Learning under Distributional Shifts. University of Maryland, College Park, MD, USA, 2021 - [c15]Neha Mukund Kalibhat, Yogesh Balaji, Soheil Feizi:
Winning Lottery Tickets in Deep Generative Models. AAAI 2021: 8038-8046 - [c14]Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi
, Soheil Feizi:
Understanding Over-parameterization in Generative Adversarial Networks. ICLR 2021 - [c13]Gowthami Somepalli, Yexin Wu, Yogesh Balaji, Bhanukiran Vinzamuri, Soheil Feizi:
Unsupervised anomaly detection with adversarial mirrored autoencoders. UAI 2021: 365-375 - [i14]Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi:
Understanding Overparameterization in Generative Adversarial Networks. CoRR abs/2104.05605 (2021) - 2020
- [c12]Phillip Pope, Yogesh Balaji, Soheil Feizi:
Adversarial Robustness of Flow-Based Generative Models. AISTATS 2020: 3795-3805 - [c11]Prithvijit Chattopadhyay, Yogesh Balaji, Judy Hoffman:
Learning to Balance Specificity and Invariance for In and Out of Domain Generalization. ECCV (9) 2020: 301-318 - [c10]Luyu Yang, Yogesh Balaji, Ser-Nam Lim, Abhinav Shrivastava:
Curriculum Manager for Source Selection in Multi-source Domain Adaptation. ECCV (14) 2020: 608-624 - [c9]Yogesh Balaji, Rama Chellappa, Soheil Feizi:
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation. NeurIPS 2020 - [i13]Yexin Wu, Yogesh Balaji, Bhanukiran Vinzamuri, Soheil Feizi:
Mirrored Autoencoders with Simplex Interpolation for Unsupervised Anomaly Detection. CoRR abs/2003.10713 (2020) - [i12]Luyu Yang, Yogesh Balaji, Ser-Nam Lim, Abhinav Shrivastava:
Curriculum Manager for Source Selection in Multi-Source Domain Adaptation. CoRR abs/2007.01261 (2020) - [i11]Prithvijit Chattopadhyay, Yogesh Balaji, Judy Hoffman:
Learning to Balance Specificity and Invariance for In and Out of Domain Generalization. CoRR abs/2008.12839 (2020) - [i10]Neha Mukund Kalibhat, Yogesh Balaji, Soheil Feizi:
Winning Lottery Tickets in Deep Generative Models. CoRR abs/2010.02350 (2020) - [i9]Yogesh Balaji, Mehrdad Farajtabar, Dong Yin, Alex Mott, Ang Li:
The Effectiveness of Memory Replay in Large Scale Continual Learning. CoRR abs/2010.02418 (2020) - [i8]Yogesh Balaji, Rama Chellappa, Soheil Feizi:
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation. CoRR abs/2010.05862 (2020)
2010 – 2019
- 2019
- [c8]Yogesh Balaji, Rama Chellappa, Soheil Feizi:
Normalized Wasserstein for Mixture Distributions With Applications in Adversarial Learning and Domain Adaptation. ICCV 2019: 6499-6507 - [c7]Yogesh Balaji, Hamed Hassani, Rama Chellappa, Soheil Feizi:
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs. ICML 2019: 414-423 - [c6]Yogesh Balaji, Martin Renqiang Min, Bing Bai, Rama Chellappa, Hans Peter Graf:
Conditional GAN with Discriminative Filter Generation for Text-to-Video Synthesis. IJCAI 2019: 1995-2001 - [i7]Yogesh Balaji, Rama Chellappa, Soheil Feizi:
Normalized Wasserstein Distance for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptation. CoRR abs/1902.00415 (2019) - [i6]Yogesh Balaji, Tom Goldstein, Judy Hoffman:
Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets. CoRR abs/1910.08051 (2019) - [i5]Phillip Pope, Yogesh Balaji, Soheil Feizi:
Adversarial Robustness of Flow-Based Generative Models. CoRR abs/1911.08654 (2019) - [i4]Wei-An Lin, Yogesh Balaji, Pouya Samangouei, Rama Chellappa:
Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Networks for Secure Inference. CoRR abs/1911.10291 (2019) - 2018
- [c5]Swami Sankaranarayanan, Yogesh Balaji, Arpit Jain, Ser Nam Lim, Rama Chellappa:
Learning From Synthetic Data: Addressing Domain Shift for Semantic Segmentation. CVPR 2018: 3752-3761 - [c4]Swami Sankaranarayanan, Yogesh Balaji, Carlos Domingo Castillo, Rama Chellappa:
Generate to Adapt: Aligning Domains Using Generative Adversarial Networks. CVPR 2018: 8503-8512 - [c3]Yogesh Balaji, Swami Sankaranarayanan, Rama Chellappa:
MetaReg: Towards Domain Generalization using Meta-Regularization. NeurIPS 2018: 1006-1016 - [i3]Yogesh Balaji, Hamed Hassani, Rama Chellappa, Soheil Feizi:
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs. CoRR abs/1810.04147 (2018) - 2017
- [c2]Vijay Rengarajan, Yogesh Balaji, A. N. Rajagopalan:
Unrolling the Shutter: CNN to Correct Motion Distortions. CVPR 2017: 2345-2353 - [i2]Swami Sankaranarayanan, Yogesh Balaji, Carlos Domingo Castillo, Rama Chellappa:
Generate To Adapt: Aligning Domains using Generative Adversarial Networks. CoRR abs/1704.01705 (2017) - [i1]Swami Sankaranarayanan, Yogesh Balaji, Arpit Jain, Ser-Nam Lim, Rama Chellappa:
Unsupervised Domain Adaptation for Semantic Segmentation with GANs. CoRR abs/1711.06969 (2017) - 2016
- [c1]Abhijith Punnappurath, Yogesh Balaji, Mahesh Mohan M. R., Ambasamudram Narayanan Rajagopalan:
Deep Decoupling of Defocus and Motion Blur for Dynamic Segmentation. ECCV (7) 2016: 750-765
Coauthor Index
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