Avalanche: an End-to-End Library for Continual Learning
원문 초록
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing interest in continual learning, especially within the deep learning community. However, algorithmic solutions are often difficult to re-implement, evaluate and port across different settings, where even results on standard benchmarks are hard to reproduce. In this work, we propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch. Avalanche is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms.
함께 살펴볼 논문
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CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental Learning
PrePrompt: Predictive prompting for class incremental learning
LoRAX: LoRA eXpandable Networks for Continual Synthetic Image Attribution
IAP: Improving Continual Learning of Vision-Language Models via Instance-Aware Prompting
Technical Report for the 5th CLVision Challenge at CVPR: Addressing the Class-Incremental with Repetition using Unlabeled Data -- 4th Place Solution
The Bayesian Approach to Continual Learning: An Overview
토론
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