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 promisin