Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification

20 March 2020

Papers citing "Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification"

8 / 8 papers shown

Title
Out-of-distribution Few-shot Learning For Edge Devices without Model Fine-tuning Xinyun Zhang Lanqing Hong OODD 33 0 0 13 Apr 2023
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification I. Ullah Dustin Carrión-Ojeda Sergio Escalera Isabelle M Guyon Mike Huisman F. Mohr Jan N van Rijn Haozhe Sun Joaquin Vanschoren P. Vu VLM 22 32 0 16 Feb 2023
NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results Dustin Carrión-Ojeda Hong Chen Adrian El Baz Sergio Escalera Chaoyu Guan Isabelle M Guyon I. Ullah Xin Eric Wang Wenwu Zhu VLM 21 6 0 31 Aug 2022
A linearized framework and a new benchmark for model selection for fine-tuning Aditya Deshpande Alessandro Achille Avinash Ravichandran Hao Li L. Zancato Charless C. Fowlkes Rahul Bhotika Stefano Soatto Pietro Perona ALM 109 46 0 29 Jan 2021
An Effective Anti-Aliasing Approach for Residual Networks C. N. Vasconcelos Hugo Larochelle Vincent Dumoulin Nicolas Le Roux Ross Goroshin SupR 25 32 0 20 Nov 2020
Scalable Transfer Learning with Expert Models J. Puigcerver C. Riquelme Basil Mustafa Cédric Renggli André Susano Pinto Sylvain Gelly Daniel Keysers N. Houlsby 27 62 0 28 Sep 2020
A Universal Representation Transformer Layer for Few-Shot Image Classification Lu Liu William L. Hamilton Guodong Long Jing Jiang Hugo Larochelle ViT 27 125 0 21 Jun 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 317 11,681 0 09 Mar 2017