HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning (w/ Author)

#hypertransformer #metalearning #deeplearning

This video contains a paper explanation and an interview with author Andrey Zhmoginov!

Few-shot learning is an interesting sub-field in meta-learning, with wide applications, such as creating personalized models based on just a handful of data points. Traditionally, approaches have followed the BERT approach where a large model is pre-trained and then fine-tuned. However, this couples the size of the final model to the size of the model that has been pre-trained. Similar problems exist with "true" meta-learners, such as MaML. HyperTransformer fundamentally decouples the meta-learner from the size of the final model by directly predicting the weights of the final model. The HyperTransformer takes the few-shot dataset as a whole into its context and predicts either one or multiple layers of a (small) ConvNet, meaning its output are the weights of the convolution filters. Interestingly, and with the correct engineering care, this actually appears to deliver promising results and can be extended in many ways.

OUTLINE:

0:00 - Intro & Overview

3:05 - Weight-generation vs Fine-tuning for few-shot learning

10:10 - HyperTransformer model architecture overview

22:30 - Why the self-attention mechanism is useful here

34:45 - Start of Interview

39:45 - Can neural networks even produce weights of other networks?

47:00 - How complex does the computational graph get?

49:45 - Why are transformers particularly good here?

58:30 - What can the attention maps tell us about the algorithm?

1:07:00 - How could we produce larger weights?

1:09:30 - Diving into experimental results

1:14:30 - What questions remain open?

Paper: https://arxiv.org/abs/2201.04182

ERRATA: I introduce Max Vladymyrov as Mark Vladymyrov

Abstract:

In this work we propose a HyperTransformer, a transformer-based model for 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. Finally, we extend our approach to a semi-supervised regime utilizing unlabeled samples in the support set and further improving few-shot performance.

Authors: Andrey Zhmoginov, Mark Sandler, Max Vladymyrov

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