Print Email Facebook Twitter TRIDENT Title TRIDENT: Transductive Variational Inference of Decoupled Latent Variables for Few Shot Classification Author Singh, Anuj (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Jamali-Rad, H. (mentor) van Gemert, J.C. (graduation committee) Leus, G.J.T. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science | Bioinformatics Date 2022-08-26 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. Subject few shot learningvariational inferencedeep learning To reference this document use: http://resolver.tudelft.nl/uuid:3187f659-b06e-4ac8-a2b1-6c4d93ac02ed Part of collection Student theses Document type master thesis Rights © 2022 Anuj Singh Files PDF Masters_Thesis.pdf 22.72 MB Close viewer /islandora/object/uuid:3187f659-b06e-4ac8-a2b1-6c4d93ac02ed/datastream/OBJ/view