Author name cluster

Jagmohan Chauhan

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5 papers

2 author rows

ICML Conference 2025 Conference Paper

Continual Generalized Category Discovery: Learning and Forgetting from a Bayesian Perspective

Hao Dai
Jagmohan Chauhan

Continual Generalized Category Discovery (C-GCD) faces a critical challenge: incrementally learning new classes from unlabeled data streams while preserving knowledge of old classes. Existing methods struggle with catastrophic forgetting, especially when unlabeled data mixes known and novel categories. We address this by analyzing C-GCD’s forgetting dynamics through a Bayesian lens, revealing that covariance misalignment between old and new classes drives performance degradation. Building on this insight, we propose Variational Bayes C-GCD (VB-CGCD), a novel framework that integrates variational inference with covariance-aware nearest-class-mean classification. VB-CGCD adaptively aligns class distributions while suppressing pseudo-label noise via stochastic variational updates. Experiments show VB-CGCD surpasses prior art by +15. 21% with the overall accuracy in the final session on standard benchmarks. We also introduce a new challenging benchmark with only 10% labeled data and extended online phases—VB-CGCD achieves a 67. 86% final accuracy, significantly higher than state-of-the-art (38. 55%), demonstrating its robust applicability across diverse scenarios. Code is available at: https: //github. com/daihao42/VB-CGCD

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ICLR Conference 2025 Conference Paper

FedTMOS: Efficient One-Shot Federated Learning with Tsetlin Machine

Shannon How Shi Qi
Jagmohan Chauhan
Geoff V. Merrett
Jonathon S. Hare

One-Shot Federated Learning (OFL) is a promising approach that reduce communication to a single round, minimizing latency and resource consumption. However, existing OFL methods often rely on Knowledge Distillation, which introduce server-side training, increasing latency. While neuron matching and model fusion techniques bypass server-side training, they struggle with alignment when heterogeneous data is present. To address these challenges, we proposed One-Shot Federated Learning with Tsetlin Machine (FedTMOS), a novel data-free OFL framework built upon the low-complexity and class-adaptive properties of the Tsetlin Machine. FedTMOS first clusters then reassigns class-specific weights to form models using an inter-class maximization approach, efficiently generating balanced server models without requiring additional training. Our extensive experiments demonstrate that FedTMOS significantly outperforms its ensemble counterpart by an average of $6.16$%, and the leading state-of-the-art OFL baselines by $7.22$% across various OFL settings. Moreover, FedTMOS achieves at least a $2.3\times$ reduction in upload communication costs and a $75\times$ reduction in server latency compared to methods requiring server-side training. These results establish FedTMOS as a highly efficient and practical solution for OFL scenarios.

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NeurIPS Conference 2025 Conference Paper

PhySwin: An Efficient and Physically-Informed Foundation Model for Multispectral Earth Observation

Chong Tang
Joseph Powell
Dirk Koch
Robert Mullins
Alex Weddell
Jagmohan Chauhan

Recent progress on Remote Sensing Foundation Models (RSFMs) aims toward universal representations for Earth observation imagery. However, current efforts often scale up in size significantly without addressing efficiency constraints critical for real-world applications (e. g. , onboard processing, rapid disaster response) or treat multispectral (MS) data as generic imagery, overlooking valuable physical priors. We introduce PhySwin, a foundation model for MS data that integrates physical priors with computational efficiency. PhySwin combines three innovations: (i) physics-informed pretraining objectives leveraging radiometric constraints to enhance feature learning; (ii) an efficient MixMAE formulation tailored to SwinV2 for low-FLOP, scalable pretraining; and (iii) token-efficient spectral embedding to retain spectral detail without increasing token counts. Pretrained on over 1M Sentinel-2 tiles, PhySwin achieves SOTA results (+1. 32\% mIoU segmentation, +0. 80\% F1 change detection) while reducing inference latency by up to 14. 4$\times$ and computational complexity by up to 43. 6$\times$ compared to ViT-based RSFMs.

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ICML Conference 2024 Conference Paper

TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge

Young D. Kwon
Rui Li 0052
Stylianos I. Venieris
Jagmohan Chauhan
Nicholas Donald Lane
Cecilia Mascolo

On-device training is essential for user personalisation and privacy. With the pervasiveness of IoT devices and microcontroller units (MCUs), this task becomes more challenging due to the constrained memory and compute resources, and the limited availability of labelled user data. Nonetheless, prior works neglect the data scarcity issue, require excessively long training time ($\textit{e. g. }$ a few hours), or induce substantial accuracy loss ($\geq$10%). In this paper, we propose TinyTrain, an on-device training approach that drastically reduces training time by selectively updating parts of the model and explicitly coping with data scarcity. TinyTrain introduces a task-adaptive sparse-update method that $\textit{dynamically}$ selects the layer/channel to update based on a multi-objective criterion that jointly captures user data, the memory, and the compute capabilities of the target device, leading to high accuracy on unseen tasks with reduced computation and memory footprint. TinyTrain outperforms vanilla fine-tuning of the entire network by 3. 6-5. 0% in accuracy, while reducing the backward-pass memory and computation cost by up to 1, 098$\times$ and 7. 68$\times$, respectively. Targeting broadly used real-world edge devices, TinyTrain achieves 9. 5$\times$ faster and 3. 5$\times$ more energy-efficient training over status-quo approaches, and 2. 23$\times$ smaller memory footprint than SOTA methods, while remaining within the 1 MB memory envelope of MCU-grade platforms.

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NeurIPS Conference 2024 Conference Paper

Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking

Yuwei Zhang
Tong Xia
Jing Han
Yu Y. Wu
Georgios Rizos
Yang Liu
Mohammed Mosuily
Jagmohan Chauhan

Respiratory audio, such as coughing and breathing sounds, has predictive power for a wide range of healthcare applications, yet is currently under-explored. The main problem for those applications arises from the difficulty in collecting large labeled task-specific data for model development. Generalizable respiratory acoustic foundation models pretrained with unlabeled data would offer appealing advantages and possibly unlock this impasse. However, given the safety-critical nature of healthcare applications, it is pivotal to also ensure openness and replicability for any proposed foundation model solution. To this end, we introduce OPERA, an OPEn Respiratory Acoustic foundation model pretraining and benchmarking system, as the first approach answering this need. We curate large-scale respiratory audio datasets ($\sim$136K samples, over 400 hours), pretrain three pioneering foundation models, and build a benchmark consisting of 19 downstream respiratory health tasks for evaluation. Our pretrained models demonstrate superior performance (against existing acoustic models pretrained with general audio on 16 out of 19 tasks) and generalizability (to unseen datasets and new respiratory audio modalities). This highlights the great promise of respiratory acoustic foundation models and encourages more studies using OPERA as an open resource to accelerate research on respiratory audio for health. The system is accessible from https: //github. com/evelyn0414/OPERA.

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