Aditya Panda
I am currently a final year doctoral student at Indian Statistical Institute.
During the last few years, I mainly worked on data efficient deep learning approaches.
Specifically I explored the different aspects of the problem Compositional Zero-Shot Learning (CZSL).
Compositional Zero-Shot Learning, focusses on recognizing new state-object compositions from limited training data.
We worked on approaches using graph convolutional networks, and transformer networks to tackle challenges in the
closed-world, open-world, and partially supervised CZSL problems. Before my PhD, I completed my M. Tech degree in Computer Science from ISI, Kolkata.
Prior to that I pursued my undergraduate studies in Electronics and Tele-Communication Engineering from Jadavpur University, Kolkata.
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IEEE Author Profile
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Compositional Zero-Shot Learning using Multi-Branch Graph Convolution and Cross-layer Knowledge Sharing
Aditya Panda,
Dipti Prasad Mukherjee
Pattern Recognition, 2024
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Isolating Features of Object and Its State for Compositional Zero-Shot Learning
Aditya Panda,
Bikash Santra,
Dipti Prasad Mukherjee
IEEE Transactions on Emerging Topics in Computational Intelligence, 2023
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Bi-Modal Compositional Network for Feature Disentanglement
Aditya Panda,
Bikash Santra,
Dipti Prasad Mukherjee
IEEE International Conference on Image Processing (ICIP), 2022
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Monocular 3D human pose estimation by multiple hypothesis prediction and joint angle supervision
Aditya Panda,
Dipti Prasad Mukherjee
IEEE International Conference on Image Processing (ICIP), 2021
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Particle swarm optimization with a modified learning strategy and blending crossover
Aditya Panda,
Rammohan Mallipeddi,
Swagatam Das,
IEEE Symposium Series on Computational Intelligence (SSCI), 2017
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Static learning particle swarm optimization with enhanced exploration and exploitation using adaptive swarm size
Aditya Panda,
Srijan Ghoshal,
Amit Konar,
Bonny Banerjee,
Atulya K Nagar,
IEEE Congress on Evolutionary Computation (CEC), 2016
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Partially Supervised Compositional Zero-Shot Learning by Locality Preserving Neighbourhood Aggregation
Aditya Panda,
Dipti Prasad Mukherjee
communicated to an IEEE Transaction
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Prompt-Driven Multi-Branch Disentanglement Network for Compositional Zero-Shot Learning
Aditya Panda,
Dipti Prasad Mukherjee
communicated to an IEEE Transaction
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- Worked as anonymous reviewer/sub-reviewer for the following journals: IEEE TPAMI, IEEE TNNLS,
IEEE TIP, IEEE TETCI, ACM Trans. on Multimedia Computing Communications and
Applications and IEEE ICIP conference
- Delivered lectures in miscellaneous deep-learning based topics in the Winter School on
Deep Learning, for the years 2022, 2023 and 2024. Also delivered lectures in deep learning and machine learning based topics in different summer and winter schools organised
by Technology Innovation Hub, Indian Statistical Institute (ISI) and also in
Center for AI & ML (CAIML), ISI.
- Performed the duty of Teaching Assistant for the Computing for Data Science subject for the students of the PGDBA course in ISI, Kolkata for the years 2022, 2023, 2024.
- Supervised a group of participants in the Summer School organised by the Electronics and Communication Sciences Unit, ISI Kolkata in the year 2022, 2023 and 2024.
The source code for this website is used from here.
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