78 citations · 103 across the 9 of their papers we have counts for
4 papers · 1 filter
Task-Adaptive Clustering for Semi-Supervised Few-Shot Classification
Jun Seo, Sung Whan Yoon, Jaekyun Moon
Few-shot learning aims to handle previously unseen tasks using only a small amount of new training data. In preparing (or meta-training) a few-shot learner, however, massive labele…
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning
Sung Whan Yoon, Do-Yeon Kim, Jun Seo +1
Learning novel concepts while preserving prior knowledge is a long-standing challenge in machine learning. The challenge gets greater when a novel task is given with only a few lab…
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
Sung Whan Yoon, Jun Seo, Jaekyun Moon
Handling previously unseen tasks after given only a few training examples continues to be a tough challenge in machine learning. We propose TapNets, neural networks augmented with…
Meta-Learner with Linear Nulling
Sung Whan Yoon, Jun Seo, Jaekyun Moon
We propose a meta-learning algorithm utilizing a linear transformer that carries out null-space projection of neural network outputs. The main idea is to construct an alternative c…