no code implementations • 2 May 2024 • Maxwell Jones, Sheng-Yu Wang, Nupur Kumari, David Bau, Jun-Yan Zhu
Both qualitative and quantitative experiments show that our method can effectively learn style while avoiding overfitting to image content, highlighting the potential of modeling such stylistic differences from a single image pair.
no code implementations • 18 Apr 2024 • Nupur Kumari, Grace Su, Richard Zhang, Taesung Park, Eli Shechtman, Jun-Yan Zhu
Model customization introduces new concepts to existing text-to-image models, enabling the generation of the new concept in novel contexts.
1 code implementation • ICCV 2023 • Nupur Kumari, Bingliang Zhang, Sheng-Yu Wang, Eli Shechtman, Richard Zhang, Jun-Yan Zhu
To achieve this goal, we propose an efficient method of ablating concepts in the pretrained model, i. e., preventing the generation of a target concept.
2 code implementations • CVPR 2023 • Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, Jun-Yan Zhu
Can we teach a model to quickly acquire a new concept, given a few examples?
1 code implementation • 6 Oct 2022 • Daohan Lu, Sheng-Yu Wang, Nupur Kumari, Rohan Agarwal, Mia Tang, David Bau, Jun-Yan Zhu
To address this need, we introduce the task of content-based model search: given a query and a large set of generative models, finding the models that best match the query.
Ranked #1 on Model Description Based Search on Generative Models
Contrastive Learning Image and Sketch based Model Retrieval +4
1 code implementation • CVPR 2022 • Nupur Kumari, Richard Zhang, Eli Shechtman, Jun-Yan Zhu
Can the collective "knowledge" from a large bank of pretrained vision models be leveraged to improve GAN training?
Ranked #1 on Image Generation on AFHQ Cat
no code implementations • 8 Dec 2020 • Puneet Mangla, Nupur Kumari, Mayank Singh, Balaji Krishnamurthy, Vineeth N Balasubramanian
Previous works have addressed training in low data setting by leveraging transfer learning and data augmentation techniques.
no code implementations • 19 Oct 2020 • Parth Patel, Nupur Kumari, Mayank Singh, Balaji Krishnamurthy
We propose a self-supervised approach (LT-GAN) to improve the generation quality and diversity of images by estimating the GAN-induced transformation (i. e. transformation induced in the generated images by perturbing the latent space of generator).
Ranked #4 on Image Generation on CelebA-HQ 128x128
no code implementations • 28 Sep 2020 • Puneet Mangla, Nupur Kumari, Mayank Singh, Vineeth N. Balasubramanian, Balaji Krishnamurthy
Recent advances in generative adversarial networks (GANs) have shown remarkable progress in generating high-quality images.
no code implementations • 4 May 2020 • Gunjan Aggarwal, Abhishek Sinha, Nupur Kumari, Mayank Singh
In this paper, we leverage models with interpretable perceptually-aligned features and show that adversarial training with low max-perturbation bound can improve the performance of models for zero-shot and weakly supervised localization tasks.
no code implementations • 15 Jan 2020 • Nupur Kumari, Siddarth R., Akash Rupela, Piyush Gupta, Balaji Krishnamurthy
This graph captures the structural characteristics of the point cloud, and its weights are determined using a Finite Markov Chain.
no code implementations • 1 Dec 2019 • Tejus Gupta, Abhishek Sinha, Nupur Kumari, Mayank Singh, Balaji Krishnamurthy
We present an algorithm for computing class-specific universal adversarial perturbations for deep neural networks.
1 code implementation • ECCV 2020 • Mayank Singh, Nupur Kumari, Puneet Mangla, Abhishek Sinha, Vineeth N. Balasubramanian, Balaji Krishnamurthy
Safe deployment of machine learning system mandates that the prediction and its explanation be reliable and robust.
Ranked #1 on Weakly-Supervised Object Localization on CUB-200-2011 (Top-1 Error Rate metric)
BIG-bench Machine Learning Weakly-Supervised Object Localization
7 code implementations • 28 Jul 2019 • Puneet Mangla, Mayank Singh, Abhishek Sinha, Nupur Kumari, Vineeth N. Balasubramanian, Balaji Krishnamurthy
A recent regularization technique - Manifold Mixup focuses on learning a general-purpose representation, robust to small changes in the data distribution.
1 code implementation • 13 May 2019 • Mayank Singh, Abhishek Sinha, Nupur Kumari, Harshitha Machiraju, Balaji Krishnamurthy, Vineeth N. Balasubramanian
We analyze the adversarially trained robust models to study their vulnerability against adversarial attacks at the level of the latent layers.
no code implementations • 11 Nov 2018 • Milan Aggarwal, Nupur Kumari, Ayush Bansal, Balaji Krishnamurthy
Generating paraphrases, that is, different variations of a sentence conveying the same meaning, is an important yet challenging task in NLP.