no code implementations • 28 Oct 2023 • Rishabh Tiwari, Durga Sivasubramanian, Anmol Mekala, Ganesh Ramakrishnan, Pradeep Shenoy
Deep networks tend to learn spurious feature-label correlations in real-world supervised learning tasks.
no code implementations • 30 Jan 2023 • Rishabh Tiwari, Pradeep Shenoy
Simplicity bias is the concerning tendency of deep networks to over-depend on simple, weakly predictive features, to the exclusion of stronger, more complex features.
1 code implementation • 14 Dec 2022 • Kushal Chauhan, Rishabh Tiwari, Jan Freyberg, Pradeep Shenoy, Krishnamurthy Dvijotham
Concept bottleneck models (CBMs) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict the final label based on the concept label predictions.
1 code implementation • 24 Nov 2022 • Saksham Aggarwal, Taneesh Gupta, Pawan Kumar Sahu, Arnav Chavan, Rishabh Tiwari, Dilip K. Prasad, Deepak K. Gupta
A comparison between SOTA trackers using CNNs, transformers as well as the combination of the two is presented to study their stability at various compression ratios.
1 code implementation • CVPR 2022 • Arnav Chavan, Rishabh Tiwari, Udbhav Bamba, Deepak K. Gupta
MetaDOCK compresses the meta-model as well as the task-specific inner models, thus providing significant reduction in model size for each task, and through constraining the number of active kernels for every task, it implicitly mitigates the issue of meta-overfitting.
no code implementations • CVPR 2022 • Rishabh Tiwari, KrishnaTeja Killamsetty, Rishabh Iyer, Pradeep Shenoy
To address this, replay-based CL approaches maintain and repeatedly retrain on a small buffer of data selected across encountered tasks.
1 code implementation • ICLR 2021 • Rishabh Tiwari, Udbhav Bamba, Arnav Chavan, Deepak K. Gupta
Structured pruning methods are among the effective strategies for extracting small resource-efficient convolutional neural networks from their dense counterparts with minimal loss in accuracy.
1 code implementation • 14 Jan 2021 • Arnav Chavan, Udbhav Bamba, Rishabh Tiwari, Deepak Gupta
We show that small base networks when rescaled, can provide performance comparable to deeper networks with as low as 6% of optimization parameters of the deeper one.
1 code implementation • 23 Mar 2020 • Suyog Jadhav, Udbhav Bamba, Arnav Chavan, Rishabh Tiwari, Aryan Raj
Endoscopic artefact detection challenge consists of 1) Artefact detection, 2) Semantic segmentation, and 3) Out-of-sample generalisation.