no code implementations • 12 Nov 2022 • Udbhav Bamba, Neeraj Anand, Saksham Aggarwal, Dilip K. Prasad, Deepak K. Gupta
To address this issue, partial binarization techniques have been developed, but a systematic approach to mixing binary and full-precision parameters in a single network is still lacking.
no code implementations • 25 Jun 2022 • Deepak K. Gupta, Udbhav Bamba, Abhishek Thakur, Akash Gupta, Suraj Sharan, Ertugrul Demir, Dilip K. Prasad
Based on the outlined issues, we introduce a novel research problem of training CNN models for very large images, and present 'UltraMNIST dataset', a simple yet representative benchmark dataset for this task.
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.
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.