3 citations · 9 across the 10 of their papers we have counts for
11 papers
Can Circuit Alignment Predict OOD Generalization?
Ayan Banerjee, Abhra Chaudhuri, Josep Llados +2
Can out-of-distribution (OOD) generalization be predicted from a trained model's weights alone, without any target-domain data? Existing representational similarity metrics (CKA, S…
A Closer Look at Multimodal Representation Collapse
Abhra Chaudhuri, Anjan Dutta, Tu Bui +1
We aim to develop a fundamental understanding of modality collapse, a recently observed empirical phenomenon wherein models trained for multimodal fusion tend to rely only on a sub…
Sebra: Debiasing Through Self-Guided Bias Ranking
Adarsh Kappiyath, Abhra Chaudhuri, Ajay Jaiswal +4
Ranking samples by fine-grained estimates of spuriosity (the degree to which spurious cues are present) has recently been shown to significantly benefit bias mitigation, over the t…
DeNetDM: Debiasing by Network Depth Modulation
Silpa Vadakkeeveetil Sreelatha, Adarsh Kappiyath, Abhra Chaudhuri +1
Neural networks trained on biased datasets tend to inadvertently learn spurious correlations, hindering generalization. We formally prove that (1) samples that exhibit spurious cor…
Learning Conditional Invariances through Non-Commutativity
Abhra Chaudhuri, Serban Georgescu, Anjan Dutta
Invariance learning algorithms that conditionally filter out domain-specific random variables as distractors, do so based only on the data semantics, and not the target domain unde…
CLIPDraw++: Text-to-Sketch Synthesis with Simple Primitives
Nityanand Mathur, Shyam Marjit, Abhra Chaudhuri +1
With the goal of understanding the visual concepts that CLIP associates with text prompts, we show that the latent space of CLIP can be visualized solely in terms of linear transfo…
Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships
Abhra Chaudhuri, Massimiliano Mancini, Zeynep Akata +1
Recent advances in fine-grained representation learning leverage local-to-global (emergent) relationships for achieving state-of-the-art results. The relational representations rel…
Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection
Swapnil Bhosale, Abhra Chaudhuri, Alex Lee Robert Williams +5
The introduction of the MUStARD dataset, and its emotion recognition extension MUStARD++, have identified sarcasm to be a multi-modal phenomenon -- expressed not only in natural la…
Data-Free Sketch-Based Image Retrieval
Abhra Chaudhuri, Ayan Kumar Bhunia, Yi-Zhe Song +1
Rising concerns about privacy and anonymity preservation of deep learning models have facilitated research in data-free learning (DFL). For the first time, we identify that for dat…
Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image Retrieval
Abhra Chaudhuri, Massimiliano Mancini, Yanbei Chen +2
Representation learning for sketch-based image retrieval has mostly been tackled by learning embeddings that discard modality-specific information. As instances from different moda…
Relational Proxies: Emergent Relationships as Fine-Grained Discriminators
Abhra Chaudhuri, Massimiliano Mancini, Zeynep Akata +1
Fine-grained categories that largely share the same set of parts cannot be discriminated based on part information alone, as they mostly differ in the way the local parts relate to…