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Task-Prior Conditional Variational Auto-Encoder for Few-Shot Image Classification

arXiv 2205.15014 ↗First posted 30 May 2022cs.CV cs.LG eess.IVcited by 0

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Abstract (arXiv)

Transductive methods always outperform inductive methods in few-shot image classification scenarios. However, the existing few-shot methods contain a latent condition: the number of samples in each class is the same, which may be unrealistic. To cope with those cases where the query shots of each class are nonuniform (i.e. nonuniform few-shot learning), we propose a Task-Prior Conditional Variational Auto-Encoder model named TP-VAE, conditioned on support shots and constrained by a task-level prior regularization. Our method obtains high performance in the more challenging nonuniform few-shot scenarios. Moreover, our method outperforms the state-of-the-art in a wide range of standard few-shot image classification scenarios. Among them, the accuracy of 1-shot increased by about 3%.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 35 of its 49 references that are in the map. First posted on arXiv 30 May 2022.

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35 of 49 references.

References · 35

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23 more
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