172 citations · 1.2k across the 80 of their papers we have counts for
96 papers
Domain-Specificity Inducing Transformers for Source-Free Domain Adaptation
Sunandini Sanyal, Ashish Ramayee Asokan, Suvaansh Bhambri +3
Conventional Domain Adaptation (DA) methods aim to learn domain-invariant feature representations to improve the target adaptation performance. However, we motivate that domain-spe…
Strata-NeRF : Neural Radiance Fields for Stratified Scenes
Ankit Dhiman, Srinath R, Harsh Rangwani +4
Neural Radiance Field (NeRF) approaches learn the underlying 3D representation of a scene and generate photo-realistic novel views with high fidelity. However, most proposed settin…
Boosting Adversarial Robustness using Feature Level Stochastic Smoothing
Sravanti Addepalli, Samyak Jain, Gaurang Sriramanan +1
Advances in adversarial defenses have led to a significant improvement in the robustness of Deep Neural Networks. However, the robust accuracy of present state-ofthe-art defenses i…
We never go out of Style: Motion Disentanglement by Subspace Decomposition of Latent Space
Rishubh Parihar, Raghav Magazine, Piyush Tiwari +1
Real-world objects perform complex motions that involve multiple independent motion components. For example, while talking, a person continuously changes their expressions, head, a…
Inspecting the Geographical Representativeness of Images from Text-to-Image Models
Abhipsa Basu, R. Venkatesh Babu, Danish Pruthi
Recent progress in generative models has resulted in models that produce both realistic as well as relevant images for most textual inputs. These models are being used to generate…
Certified Adversarial Robustness Within Multiple Perturbation Bounds
Soumalya Nandi, Sravanti Addepalli, Harsh Rangwani +1
Randomized smoothing (RS) is a well known certified defense against adversarial attacks, which creates a smoothed classifier by predicting the most likely class under random noise…
Continual Domain Adaptation through Pruning-aided Domain-specific Weight Modulation
Prasanna B, Sunandini Sanyal, R. Venkatesh Babu
In this paper, we propose to develop a method to address unsupervised domain adaptation (UDA) in a practical setting of continual learning (CL). The goal is to update the model on…
NoisyTwins: Class-Consistent and Diverse Image Generation through StyleGANs
Harsh Rangwani, Lavish Bansal, Kartik Sharma +3
StyleGANs are at the forefront of controllable image generation as they produce a latent space that is semantically disentangled, making it suitable for image editing and manipulat…
CapsFlow: Optical Flow Estimation with Capsule Networks
Rahul Chand, Rajat Arora, K Ram Prabhakar +1
We present a framework to use recently introduced Capsule Networks for solving the problem of Optical Flow, one of the fundamental computer vision tasks. Most of the existing state…
DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural Networks
Samyak Jain, Sravanti Addepalli, Pawan Sahu +2
Generalization of neural networks is crucial for deploying them safely in the real world. Common training strategies to improve generalization involve the use of data augmentations…
Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data
Harsh Rangwani, Sumukh K Aithal, Mayank Mishra +1
Real-world datasets exhibit imbalances of varying types and degrees. Several techniques based on re-weighting and margin adjustment of loss are often used to enhance the performanc…
Subsidiary Prototype Alignment for Universal Domain Adaptation
Jogendra Nath Kundu, Suvaansh Bhambri, Akshay Kulkarni +3
Universal Domain Adaptation (UniDA) deals with the problem of knowledge transfer between two datasets with domain-shift as well as category-shift. The goal is to categorize unlabel…
Efficient and Effective Augmentation Strategy for Adversarial Training
Sravanti Addepalli, Samyak Jain, R. Venkatesh Babu
Adversarial training of Deep Neural Networks is known to be significantly more data-hungry when compared to standard training. Furthermore, complex data augmentations such as AutoA…
Towards Efficient and Effective Self-Supervised Learning of Visual Representations
Sravanti Addepalli, Kaushal Bhogale, Priyam Dey +1
Self-supervision has emerged as a propitious method for visual representation learning after the recent paradigm shift from handcrafted pretext tasks to instance-similarity based a…
Scaling Adversarial Training to Large Perturbation Bounds
Sravanti Addepalli, Samyak Jain, Gaurang Sriramanan +1
The vulnerability of Deep Neural Networks to Adversarial Attacks has fuelled research towards building robust models. While most Adversarial Training algorithms aim at defending at…
Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks
Sravanti Addepalli, Anshul Nasery, R. Venkatesh Babu +2
Deep Neural Networks are known to be brittle to even minor distribution shifts compared to the training distribution. While one line of work has demonstrated that Simplicity Bias (…
Improving GANs for Long-Tailed Data through Group Spectral Regularization
Harsh Rangwani, Naman Jaswani, Tejan Karmali +2
Deep long-tailed learning aims to train useful deep networks on practical, real-world imbalanced distributions, wherein most labels of the tail classes are associated with a few sa…
Hierarchical Semantic Regularization of Latent Spaces in StyleGANs
Tejan Karmali, Rishubh Parihar, Susmit Agrawal +4
Progress in GANs has enabled the generation of high-resolution photorealistic images of astonishing quality. StyleGANs allow for compelling attribute modification on such images vi…
Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation
Jogendra Nath Kundu, Suvaansh Bhambri, Akshay Kulkarni +3
The prime challenge in unsupervised domain adaptation (DA) is to mitigate the domain shift between the source and target domains. Prior DA works show that pretext tasks could be us…
Segmentation Guided Deep HDR Deghosting
K. Ram Prabhakar, Susmit Agrawal, R. Venkatesh Babu
We present a motion segmentation guided convolutional neural network (CNN) approach for high dynamic range (HDR) image deghosting. First, we segment the moving regions in the input…
A Closer Look at Smoothness in Domain Adversarial Training
Harsh Rangwani, Sumukh K Aithal, Mayank Mishra +2
Domain adversarial training has been ubiquitous for achieving invariant representations and is used widely for various domain adaptation tasks. In recent times, methods converging…
Balancing Discriminability and Transferability for Source-Free Domain Adaptation
Jogendra Nath Kundu, Akshay Kulkarni, Suvaansh Bhambri +4
Conventional domain adaptation (DA) techniques aim to improve domain transferability by learning domain-invariant representations; while concurrently preserving the task-discrimina…
Towards Data-Free Model Stealing in a Hard Label Setting
Sunandini Sanyal, Sravanti Addepalli, R. Venkatesh Babu
Machine learning models deployed as a service (MLaaS) are susceptible to model stealing attacks, where an adversary attempts to steal the model within a restricted access framework…
LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity
Tejan Karmali, Abhinav Atrishi, Sai Sree Harsha +3
In this work, we introduce LEAD, an approach to discover landmarks from an unannotated collection of category-specific images. Existing works in self-supervised landmark detection…