Transductive Decoupled Variational Inference for Few-Shot Classification

TL;DR

TRIDENT introduces a probabilistic variational inference network with a novel attention-based transductive module for few-shot classification, achieving state-of-the-art results on standard and cross-domain datasets.

Contribution

It proposes a decoupled variational inference framework with a new attention-based transductive feature extractor for improved few-shot learning performance.

Findings

Sets new state-of-the-art on miniImageNet and tieredImageNet datasets.

Achieves up to 20% improvement in cross-domain miniImageNet to CUB tasks.

Demonstrates effectiveness with simple backbone architectures.

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. Code and experimentation can be found in our GitHub repository: https://github.com/anujinho/trident