3.3k citations · 4.2k across the 69 of their papers we have counts for
13 papers · 1 filter
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…
Context-Conditional Adaptation for Recognizing Unseen Classes in Unseen Domains
Puneet Mangla, Shivam Chandhok, Vineeth N Balasubramanian +1
Recent progress towards designing models that can generalize to unseen domains (i.e domain generalization) or unseen classes (i.e zero-shot learning) has embarked interest towards…
Learn from Anywhere: Rethinking Generalized Zero-Shot Learning with Limited Supervision
Gaurav Bhatt, Shivam Chandhok, Vineeth N Balasubramanian
A common problem with most zero and few-shot learning approaches is they suffer from bias towards seen classes resulting in sub-optimal performance. Existing efforts aim to utilize…
Structured Latent Embeddings for Recognizing Unseen Classes in Unseen Domains
Shivam Chandhok, Sanath Narayan, Hisham Cholakkal +4
The need to address the scarcity of task-specific annotated data has resulted in concerted efforts in recent years for specific settings such as zero-shot learning (ZSL) and domain…
Canonical Saliency Maps: Decoding Deep Face Models
Thrupthi Ann John, Vineeth N Balasubramanian, C V Jawahar
As Deep Neural Network models for face processing tasks approach human-like performance, their deployment in critical applications such as law enforcement and access control has se…
Instance-wise Causal Feature Selection for Model Interpretation
Pranoy Panda, Sai Srinivas Kancheti, Vineeth N Balasubramanian
We formulate a causal extension to the recently introduced paradigm of instance-wise feature selection to explain black-box visual classifiers. Our method selects a subset of input…
Improving Attribution Methods by Learning Submodular Functions
Piyushi Manupriya, Tarun Ram Menta, J. Saketha Nath +1
This work explores the novel idea of learning a submodular scoring function to improve the specificity/selectivity of existing feature attribution methods. Submodular scores are na…
Towards Open World Object Detection
K J Joseph, Salman Khan, Fahad Shahbaz Khan +1
Humans have a natural instinct to identify unknown object instances in their environments. The intrinsic curiosity about these unknown instances aids in learning about them, when t…