Unsupervised Parameter Efficient Source-free Post-pretraining

• URL: http://arxiv.org/abs/2502.21313v1
• Date: Fri, 28 Feb 2025 18:54:51 GMT
• Title: Unsupervised Parameter Efficient Source-free Post-pretraining
• Authors: Abhishek Jha, Tinne Tuytelaars, Yuki M. Asano,
• Abstract summary: We introduce UpStep, an Unsupervised.<n>Source-free post-pretraining approach to adapt a base model from a source domain to a target domain.<n>We use various general backbone architectures, both supervised and unsupervised, trained on Imagenet as our base model.
• Score: 52.27955794126508
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Following the success in NLP, the best vision models are now in the billion parameter ranges. Adapting these large models to a target distribution has become computationally and economically prohibitive. Addressing this challenge, we introduce UpStep, an Unsupervised Parameter-efficient Source-free post-pretraining approach, designed to efficiently adapt a base model from a source domain to a target domain: i) we design a self-supervised training scheme to adapt a pretrained model on an unlabeled target domain in a setting where source domain data is unavailable. Such source-free setting comes with the risk of catastrophic forgetting, hence, ii) we propose center vector regularization (CVR), a set of auxiliary operations that minimize catastrophic forgetting and additionally reduces the computational cost by skipping backpropagation in 50\% of the training iterations. Finally iii) we perform this adaptation process in a parameter-efficient way by adapting the pretrained model through low-rank adaptation methods, resulting in a fraction of parameters to optimize. We utilize various general backbone architectures, both supervised and unsupervised, trained on Imagenet as our base model and adapt them to a diverse set of eight target domains demonstrating the adaptability and generalizability of our proposed approach.

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