Learning from Few Samples: A Survey

Learning from Few Samples: A Survey

30 July 2020

Hugo Terashima-Marín

Peyman Najafirad

Papers citing "Learning from Few Samples: A Survey"

8 / 8 papers shown

Title
Large Language Models for Cyber Security: A Systematic Literature Review HanXiang Xu Shenao Wang Ningke Li K. Wang Yanjie Zhao Kai Chen Ting Yu Yang Janet Liu H. Wang 29 23 0 08 May 2024
Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems? Davide Buffelli Ashish Gupta Agnieszka Strzalka Vassilis Plachouras OffRL LRM 19 1 0 16 Aug 2023
$Δ$ -Patching: A Framework for Rapid Adaptation of Pre-trained Convolutional Networks without Base Performance Loss Chaitanya Devaguptapu Samarth Sinha K. J. Joseph V. Balasubramanian Animesh Garg 60 1 0 26 Mar 2023
Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples Jianjian Qin Chunzhi Gu Junzhou Yu Chaoxi Zhang 3DPC 21 12 0 31 Oct 2022
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities Yisheng Song Ting-Yuan Wang S. Mondal J. P. Sahoo SLR 36 342 0 13 May 2022
Recommender systems based on graph embedding techniques: A comprehensive review Yue Deng 32 22 0 20 Sep 2021
Delta-encoder: an effective sample synthesis method for few-shot object recognition Eli Schwartz Leonid Karlinsky J. Shtok Sivan Harary Mattias Marder Rogerio Feris Abhishek Kumar Raja Giryes A. Bronstein 181 351 0 12 Jun 2018
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 278 11,677 0 09 Mar 2017