no code implementations • 2 Aug 2023 • Siladittya Manna, Soumitri Chattopadhyay, Rakesh Dey, Saumik Bhattacharya, Umapada Pal
We propose a cosine similarity-dependent temperature scaling function to effectively optimize the distribution of the samples in the feature space.
1 code implementation • 1 May 2023 • Subhajit Maity, Sanket Biswas, Siladittya Manna, Ayan Banerjee, Josep Lladós, Saumik Bhattacharya, Umapada Pal
Document layout analysis is a known problem to the documents research community and has been vastly explored yielding a multitude of solutions ranging from text mining, and recognition to graph-based representation, visual feature extraction, etc.
no code implementations • 31 Jul 2022 • Siladittya Manna, Rakesh Dey, Souvik Chakraborty
Supervised Learning algorithms require a large volumes of balanced data to learn robust representations.
no code implementations • 26 Feb 2022 • Siladittya Manna, Soumitri Chattopadhyay, Saumik Bhattacharya, Umapada Pal
Writer independent offline signature verification is one of the most challenging tasks in pattern recognition as there is often a scarcity of training data.
1 code implementation • 25 Jan 2022 • Soumitri Chattopadhyay, Siladittya Manna, Saumik Bhattacharya, Umapada Pal
This results in robust discriminative learning of the embedding space.
no code implementations • 24 Nov 2021 • Siladittya Manna, Umapada Pal, Saumik Bhattacharya
After 200 epochs of pre-training with ResNet-18 as the backbone, the proposed model achieves an accuracy of 86. 2\%, 58. 18\%, 77. 49\%, and 30. 87\% on CIFAR-10, CIFAR-100, STL-10, and Tiny-ImageNet datasets, respectively, and surpasses the SOTA contrastive baseline by 1. 23\%, 3. 57\%, 2. 00\%, and 0. 33\%, respectively.
2 code implementations • 21 Apr 2021 • Siladittya Manna, Saumik Bhattacharya, Umapada Pal
The downstream task in our paper is a class imbalanced multi-label classification.
Ranked #2 on Multi-Label Classification on MRNet
1 code implementation • 15 Jul 2020 • Siladittya Manna, Saumik Bhattacharya, Umapada Pal
In this paper, we propose a self-supervised learning approach to learn transferable features from MR video clips by enforcing the model to learn anatomical features.