HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
This addresses the challenge of adapting models to new tasks with limited data, particularly for small CNN architectures, though it appears incremental as it builds on existing Transformer and few-shot learning techniques.
The authors tackled the problem of few-shot learning by proposing HyperTransformer, a model that generates CNN weights directly from support samples, achieving competitive or better results than state-of-the-art methods for both supervised and semi-supervised tasks.
In this work we propose a HyperTransformer, a Transformer-based model for supervised and semi-supervised few-shot learning that generates weights of a convolutional neural network (CNN) directly from support samples. Since the dependence of a small generated CNN model on a specific task is encoded by a high-capacity Transformer model, we effectively decouple the complexity of the large task space from the complexity of individual tasks. Our method is particularly effective for small target CNN architectures where learning a fixed universal task-independent embedding is not optimal and better performance is attained when the information about the task can modulate all model parameters. For larger models we discover that generating the last layer alone allows us to produce competitive or better results than those obtained with state-of-the-art methods while being end-to-end differentiable.