FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification
This addresses the problem of efficient model updates for personalization and federated learning in image classification, though it is incremental as it combines existing transfer and meta-learning ideas.
The paper tackles the challenge of few-shot learning in personalized and federated image classification by developing FiT, which achieves state-of-the-art accuracy on the VTAB-1k benchmark with fewer than 1% of updateable parameters and outperforms Big Transfer at low-shot.
Modern deep learning systems are increasingly deployed in situations such as personalization and federated learning where it is necessary to support i) learning on small amounts of data, and ii) communication efficient distributed training protocols. In this work, we develop FiLM Transfer (FiT) which fulfills these requirements in the image classification setting by combining ideas from transfer learning (fixed pretrained backbones and fine-tuned FiLM adapter layers) and meta-learning (automatically configured Naive Bayes classifiers and episodic training) to yield parameter efficient models with superior classification accuracy at low-shot. The resulting parameter efficiency is key for enabling few-shot learning, inexpensive model updates for personalization, and communication efficient federated learning. We experiment with FiT on a wide range of downstream datasets and show that it achieves better classification accuracy than the leading Big Transfer (BiT) algorithm at low-shot and achieves state-of-the art accuracy on the challenging VTAB-1k benchmark, with fewer than 1% of the updateable parameters. Finally, we demonstrate the parameter efficiency and superior accuracy of FiT in distributed low-shot applications including model personalization and federated learning where model update size is an important performance metric.