Papers › HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning

HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning

11 Jan 2022arXiv:2201.04182archive 2025-07-28

Andrey Zhmoginov, Mark Sandler, Max Vladymyrov

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.

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Code

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Tasks

Few-Shot Image Classification Few-Shot Learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Few-Shot Image Classification	OMNIGLOT - 1-Shot, 20-way	MAML++	Accuracy	97.7	#5 of 20	Archive leaderboard	report
Few-Shot Image Classification	OMNIGLOT - 5-Shot, 20-way	MAML++	Accuracy	99.3%	#7 of 19	Archive leaderboard	report
Few-Shot Image Classification	Tiered ImageNet 5-way (5-shot)	HyperTransformer	Accuracy	73.9%	#50 of 51	Archive leaderboard	report
Few-Shot Image Classification	Tiered ImageNet 5-way (5-shot)	RFS	Accuracy	73.2%	#51 of 51	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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