1 code implementation • 2 Aug 2023 • Siladittya Manna, Soumitri Chattopadhyay, Rakesh Dey, Saumik Bhattacharya, Umapada Pal
In contemporary self-supervised contrastive algorithms like SimCLR, MoCo, etc., the task of balancing attraction between two semantically similar samples and repulsion between two samples of different classes is primarily affected by the presence of hard negative samples.
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.