PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.
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Updated
Nov 5, 2025 - Jupyter Notebook
PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.
RanPAC: Random Projections and Pre-trained Models for Continual Learning - Official code repository for NeurIPS 2023 Published Paper
[NeurIPS 2023] A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm
Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning (CVPR 2025)
[ICPR 2024] Exemplar-free continual deepfake detector that leverages CLIP and domain-specific multi-modal prompts
Dual Cognitive Architecture: Incorporating Biases and Multi-Memory Systems for Lifelong Learning
[TMLR 2026] FedProTIP: Task-Agnostic Federated Continual Learning via Replay-Free Gradient Projection
🏷️ multi-label cardinality-incremental experiments of our CVWW 2026 submission.
CEL Continual Learning
Implementation of Dark Experience Replay with Reservoir Sampling from scratch, benchmarked on CIL, TIL and DIL.
Production continual learning in PyTorch — three scenarios, five methods, complete benchmarks.
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