Avalanche: A PyTorch Library for Deep Continual Learning
원문 초록
Continual learning is the problem of learning from a nonstationary stream of data, a fundamental issue for sustainable and efficient training of deep neural networks over time. Unfortunately, deep learning libraries only provide primitives for offline training, assuming that model's architecture and data are fixed. Avalanche is an open source library maintained by the ContinualAI non-profit organization that extends PyTorch by providing first-class support for dynamic architectures, streams of datasets, and incremental training and evaluation methods. Avalanche provides a large set of predefined benchmarks and training algorithms and it is easy to extend and modular while supporting a wide range of continual learning scenarios. Documentation is available at \url{https://avalanche.continualai.org}.
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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
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