2 citations · 2 across the 6 of their papers we have counts for
8 papers
Not Another Text Benchmark: Putting the "Visual" Back in Visual Question Answering for Large Video Models
Rwiddhi Chakraborty, Yinong, Yinong Oliver Wang +9
Large video models have exhibited impressive performance on a wide range of visual question answering tasks, owing to the rise of powerful, pretrained text and vision encoders. The…
Suppressing Non-Semantic Noise in Masked Image Modeling Representations
Martine Hjelkrem-Tan, Marius Aasan, Rwiddhi Chakraborty +3
Masked Image Modeling (MIM) has become a ubiquitous self-supervised vision paradigm. In this work, we show that MIM objectives cause the learned representations to retain non-seman…
Why Prototypes Collapse: Diagnosing and Preventing Partial Collapse in Prototypical Self-Supervised Learning
Gabriel Y. Arteaga, Marius Aasan, Rwiddhi Chakraborty +4
Prototypical self-supervised learning methods consistently suffer from partial prototype collapse, where multiple prototypes converge to nearly identical representations. This unde…
Visual Data Diagnosis and Debiasing with Concept Graphs
Rwiddhi Chakraborty, Yinong Wang, Jialu Gao +3
The widespread success of deep learning models today is owed to the curation of extensive datasets significant in size and complexity. However, such models frequently pick up inher…
ExMap: Leveraging Explainability Heatmaps for Unsupervised Group Robustness to Spurious Correlations
Rwiddhi Chakraborty, Adrian Sletten, Michael Kampffmeyer
Group robustness strategies aim to mitigate learned biases in deep learning models that arise from spurious correlations present in their training datasets. However, most existing…
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings
Daniel J. Trosten, Rwiddhi Chakraborty, Sigurd Løkse +3
Distance-based classification is frequently used in transductive few-shot learning (FSL). However, due to the high-dimensionality of image representations, FSL classifiers are pron…
RC2020 Report: Learning De-biased Representations with Biased Representations
Rwiddhi Chakraborty, Shubhayu Das
As part of the ML Reproducibility Challenge 2020, we investigated the ICML 2020 paper "Learning De-biased Representations with Biased Representations" by Bahng et al., where the au…
A Review and Refinement of Surprise Adequacy
Michael Weiss, Rwiddhi Chakraborty, Paolo Tonella
Surprise Adequacy (SA) is one of the emerging and most promising adequacy criteria for Deep Learning (DL) testing. As an adequacy criterion, it has been used to assess the strength…