activity
20242026
most citedPIP: Prototypes-Injected Prompt for Federated Class Incremental Learning

1 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI2026

HADT: A Heterogeneous Multi-Agent Differential Transformer for Autonomous Earth Observation Satellite Cluster

Mohamad A. Hady, Muhammad Anwar Masum, Siyi Hu +3

This work addresses the problem of autonomous resource management in heterogeneous satellite cluster conducting Earth Observation (EO) missions including optical and Synthetic Aper…

cs.LG2025

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning

M. Anwar Ma'sum, Mahardhika Pratama, Savitha Ramasamy +3

The data privacy constraint in online continual learning (OCL), where the data can be seen only once, complicates the catastrophic forgetting problem in streaming data. A common ap…

cs.LG2025

Latest Advancements Towards Catastrophic Forgetting under Data Scarcity: A Comprehensive Survey on Few-Shot Class Incremental Learning

M. Anwar Ma'sum, Mahardhika Pratama, Igor Skrjanc

Data scarcity significantly complicates the continual learning problem, i.e., how a deep neural network learns in dynamic environments with very few samples. However, the latest pr…

cs.LG20241 cited

PIP: Prototypes-Injected Prompt for Federated Class Incremental Learning

Muhammad Anwar Ma'sum, Mahardhika Pratama, Savitha Ramasamy +3

Federated Class Incremental Learning (FCIL) is a new direction in continual learning (CL) for addressing catastrophic forgetting and non-IID data distribution simultaneously. Exist…

cs.CV20241 cited

Unsupervised Few-Shot Continual Learning for Remote Sensing Image Scene Classification

Muhammad Anwar Ma'sum, Mahardhika Pratama, Ramasamy Savitha +3

A continual learning (CL) model is desired for remote sensing image analysis because of varying camera parameters, spectral ranges, resolutions, etc. There exist some recent initia…