TRIDENT
Transductive Variational Inference of Decoupled Latent Variables for Few Shot Classification
A.R. Singh (TU Delft - Electrical Engineering, Mathematics and Computer Science)
H. Jamali Rad – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
J.C. van Gemert – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
Geert Leus – Graduation committee member (TU Delft - Signal Processing Systems)
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Abstract
The versatility to learn from a handful of samples is the hallmark of human intelligence. Few-shot learning is an endeavour to transcend this capability down to machines. Inspired by the promise and power of probabilistic deep learning, we propose a novel variational inference network for few-shot classification (coined as TRIDENT) to decouple the representation of an image into semantic and label latent variables, and simultaneously infer them in an intertwined fashion. To induce task-awareness, as part of the inference mechanics of TRIDENT, we exploit information across both query and support images of a few-shot task using a novel built-in attention-based transductive feature extraction module (we call AttFEX). Our extensive experimental results corroborate the efficacy of TRIDENT and demonstrate that, using the simplest of backbones, it sets a new state-of-the-art in the most commonly adopted datasets miniImageNet and tieredImageNet (offering up to 4% and 5% improvements, respectively), as well as for the recent challenging cross-domain miniImagenet --> CUB scenario offering a significant margin (up to 20% improvement) beyond the best existing cross-domain baselines.