Continual Learning with Pretrained Backbones by Tuning in the Input Space

• URL: http://arxiv.org/abs/2306.02947v2
• Date: Thu, 8 Jun 2023 07:43:36 GMT
• Title: Continual Learning with Pretrained Backbones by Tuning in the Input Space
• Authors: Simone Marullo and Matteo Tiezzi and Marco Gori and Stefano Melacci and Tinne Tuytelaars
• Abstract summary: The intrinsic difficulty in adapting deep learning models to non-stationary environments limits the applicability of neural networks to real-world tasks. We propose a novel strategy to make the fine-tuning procedure more effective, by avoiding to update the pre-trained part of the network and learning not only the usual classification head, but also a set of newly-introduced learnable parameters.
• Score: 44.97953547553997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The intrinsic difficulty in adapting deep learning models to non-stationary environments limits the applicability of neural networks to real-world tasks. This issue is critical in practical supervised learning settings, such as the ones in which a pre-trained model computes projections toward a latent space where different task predictors are sequentially learned over time. As a matter of fact, incrementally fine-tuning the whole model to better adapt to new tasks usually results in catastrophic forgetting, with decreasing performance over the past experiences and losing valuable knowledge from the pre-training stage. In this paper, we propose a novel strategy to make the fine-tuning procedure more effective, by avoiding to update the pre-trained part of the network and learning not only the usual classification head, but also a set of newly-introduced learnable parameters that are responsible for transforming the input data. This process allows the network to effectively leverage the pre-training knowledge and find a good trade-off between plasticity and stability with modest computational efforts, thus especially suitable for on-the-edge settings. Our experiments on four image classification problems in a continual learning setting confirm the quality of the proposed approach when compared to several fine-tuning procedures and to popular continual learning methods.

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