3.3k citations · 4.2k across the 69 of their papers we have counts for
80 papers
On Counterfactual Data Augmentation Under Confounding
Abbavaram Gowtham Reddy, Saketh Bachu, Saloni Dash +3
Counterfactual data augmentation has recently emerged as a method to mitigate confounding biases in the training data. These biases, such as spurious correlations, arise due to var…
-Patching: A Framework for Rapid Adaptation of Pre-trained Convolutional Networks without Base Performance Loss
Chaitanya Devaguptapu, Samarth Sinha, K J Joseph +2
Models pre-trained on large-scale datasets are often fine-tuned to support newer tasks and datasets that arrive over time. This process necessitates storing copies of the model ove…
Towards Learning and Explaining Indirect Causal Effects in Neural Networks
Abbavaram Gowtham Reddy, Saketh Bachu, Harsharaj Pathak +4
Recently, there has been a growing interest in learning and explaining causal effects within Neural Network (NN) models. By virtue of NN architectures, previous approaches consider…
Towards Estimating Transferability using Hard Subsets
Tarun Ram Menta, Surgan Jandial, Akash Patil +6
As transfer learning techniques are increasingly used to transfer knowledge from the source model to the target task, it becomes important to quantify which source models are suita…
On the Robustness of Explanations of Deep Neural Network Models: A Survey
Amlan Jyoti, Karthik Balaji Ganesh, Manoj Gayala +3
Explainability has been widely stated as a cornerstone of the responsible and trustworthy use of machine learning models. With the ubiquitous use of Deep Neural Network (DNN) model…
NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation
Abbavaram Gowtham Reddy, Vineeth N Balasubramanian
Causal effect estimation from observational data is a central problem in causal inference. Methods based on potential outcomes framework solve this problem by exploiting inductive…
Distilling the Undistillable: Learning from a Nasty Teacher
Surgan Jandial, Yash Khasbage, Arghya Pal +2
The inadvertent stealing of private/sensitive information using Knowledge Distillation (KD) has been getting significant attention recently and has guided subsequent defense effort…
Counterfactual Generation Under Confounding
Abbavaram Gowtham Reddy, Saloni Dash, Amit Sharma +1
A machine learning model, under the influence of observed or unobserved confounders in the training data, can learn spurious correlations and fail to generalize when deployed. For…
ARUBA: An Architecture-Agnostic Balanced Loss for Aerial Object Detection
Rebbapragada V C Sairam, Monish Keswani, Uttaran Sinha +2
Deep neural networks tend to reciprocate the bias of their training dataset. In object detection, the bias exists in the form of various imbalances such as class, background-foregr…
Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer
Arjun Ashok, K J Joseph, Vineeth Balasubramanian
In class-incremental learning, the model is expected to learn new classes continually while maintaining knowledge on previous classes. The challenge here lies in preserving the mod…
Learning Modular Structures That Generalize Out-of-Distribution
Arjun Ashok, Chaitanya Devaguptapu, Vineeth Balasubramanian
Out-of-distribution (O.O.D.) generalization remains to be a key challenge for real-world machine learning systems. We describe a method for O.O.D. generalization that, through trai…
Novel Class Discovery without Forgetting
K J Joseph, Sujoy Paul, Gaurav Aggarwal +4
Humans possess an innate ability to identify and differentiate instances that they are not familiar with, by leveraging and adapting the knowledge that they have acquired so far. I…
INDIGO: Intrinsic Multimodality for Domain Generalization
Puneet Mangla, Shivam Chandhok, Milan Aggarwal +2
For models to generalize under unseen domains (a.k.a domain generalization), it is crucial to learn feature representations that are domain-agnostic and capture the underlying sema…
On Conditioning the Input Noise for Controlled Image Generation with Diffusion Models
Vedant Singh, Surgan Jandial, Ayush Chopra +3
Conditional image generation has paved the way for several breakthroughs in image editing, generating stock photos and 3-D object generation. This continues to be a significant are…
Proto2Proto: Can you recognize the car, the way I do?
Monish Keswani, Sriranjani Ramakrishnan, Nishant Reddy +1
Prototypical methods have recently gained a lot of attention due to their intrinsic interpretable nature, which is obtained through the prototypes. With growing use cases of model…
Spacing Loss for Discovering Novel Categories
K J Joseph, Sujoy Paul, Gaurav Aggarwal +4
Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances fro…
Unseen Classes at a Later Time? No Problem
Hari Chandana Kuchibhotla, Sumitra S Malagi, Shivam Chandhok +1
Recent progress towards learning from limited supervision has encouraged efforts towards designing models that can recognize novel classes at test time (generalized zero-shot learn…
Energy-based Latent Aligner for Incremental Learning
K J Joseph, Salman Khan, Fahad Shahbaz Khan +2
Deep learning models tend to forget their earlier knowledge while incrementally learning new tasks. This behavior emerges because the parameter updates optimized for the new tasks…
On Causally Disentangled Representations
Abbavaram Gowtham Reddy, Benin Godfrey L, Vineeth N Balasubramanian
Representation learners that disentangle factors of variation have already proven to be important in addressing various real world concerns such as fairness and interpretability. I…
Feature Generation for Long-tail Classification
Rahul Vigneswaran, Marc T. Law, Vineeth N. Balasubramanian +1
The visual world naturally exhibits an imbalance in the number of object or scene instances resulting in a \emph{long-tailed distribution}. This imbalance poses significant challen…
Matching Learned Causal Effects of Neural Networks with Domain Priors
Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N Balasubramanian +1
A trained neural network can be interpreted as a structural causal model (SCM) that provides the effect of changing input variables on the model's output. However, if training data…
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning Approach
Anindya Sarkar, Anirban Sarkar, Sowrya Gali +1
Current SOTA adversarially robust models are mostly based on adversarial training (AT) and differ only by some regularizers either at inner maximization or outer minimization steps…
Multi-Domain Incremental Learning for Semantic Segmentation
Prachi Garg, Rohit Saluja, Vineeth N Balasubramanian +3
Recent efforts in multi-domain learning for semantic segmentation attempt to learn multiple geographical datasets in a universal, joint model. A simple fine-tuning experiment perfo…
A Framework for Learning Ante-hoc Explainable Models via Concepts
Anirban Sarkar, Deepak Vijaykeerthy, Anindya Sarkar +1
Self-explaining deep models are designed to learn the latent concept-based explanations implicitly during training, which eliminates the requirement of any post-hoc explanation gen…