Publications & Research

Exploring the frontiers of Computer Vision and Deep Learning

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Publication Statistics

2
Total Publications
1
Conference Papers
1
Journal Articles
5
Under Review

Preprints & Under Review

C-LEAD: Contrastive Learning for Enhanced Adversarial Defense Preprint Under Review

Suklav Ghosh, Sonal Kumar, Arijit Sur
arXiv preprint arXiv:2510.27249, 2025
This work introduces a novel contrastive learning approach for enhancing adversarial robustness in deep neural networks. We demonstrate significant improvements in defense against various attack methods while maintaining competitive clean accuracy.
arXiv Paper

Trans-defense: Transformer-based Denoiser for Adversarial Defense with Spatial-Frequency Domain Representation Preprint Under Review

A Pramanick, M Bansal, U Srivastava, Suklav Ghosh, Arijit Sur
arXiv preprint arXiv:2510.27245, 2025
This work presents a novel transformer-based denoising approach for adversarial defense that operates in both spatial and frequency domains. The method effectively mitigates adversarial perturbations while preserving essential image features.
arXiv Paper

Multi-source Transfer Learning & Self-Supervised Learning for Enhanced Visual Representation In Progress

Suklav Ghosh, Arijit Sur, Pinaki Mitra
Under Preparation, 2025
An investigation into combining multiple source domains for transfer learning with self-supervised learning techniques. The work aims to improve visual representation learning across diverse domains and tasks.
Preprint Coming Soon

Conference Papers

A Non-Recursive Space-Efficient Blind Approach to Find All Possible Solutions to the N-Queens Problem Published

Suklav Ghosh, Sarbajit Manna
International Conference on Innovations in Data Analytics (ICIDA), Springer Nature Singapore, 2023
This paper presents a novel non-recursive, space-efficient algorithm for solving the N-Queens problem. The proposed blind approach efficiently finds all possible solutions while maintaining optimal space complexity, making it suitable for large-scale applications.
Springer Link

Journal Articles

A Model-Based Approach For Flight Delay Prediction Published

A Dutta, M Nandi, S Sarkar, S Hota, Suklav Ghosh
Indian Journal of Applied Research, 2023
A comprehensive study on flight delay prediction using machine learning models. The research explores various factors contributing to flight delays and develops predictive models to assist in aviation management and passenger planning.
Journal Article

Research Focus Areas

Multimodal Learning

Vision-language models and cross-modal understanding systems

Computer Vision

Image analysis, object detection, and visual understanding systems

Self-Supervised Learning

Learning representations from unlabeled data using contrastive methods

Deep Learning

Neural networks, representation learning, and model optimization techniques

Future Directions

My current research focuses on advancing the robustness and efficiency of deep learning models through novel self-supervised learning approaches and multi-modal understanding. I am particularly interested in developing methods that can learn from minimal supervision while maintaining strong performance across diverse domains.

Multimodal Learning Few-Shot Learning Domain Adaptation Robust AI
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