Few-shot learning 是一种Meta Learning。 Meta Learning 就是自主学习。 1.Supervised Learning vs. Few-Shot Learning 与监督学习相比,Few-Shot Learning 的Query Sample 的类别也是未知的。 Support Set 通常是一个二维矩阵的形式。 k-way 表示类别的个数 n-shot 表示每个类样本的个数 变化关系如上图所示。
首先我们先来说说Transductive Few-Shot Learning,这个方向有合理的一面也有不合理的一面(后面再细说)。一般我们谈到Few-Shot Learning (FSL)听得最多的是这么个任务,我有五类,每类有1个样本(或者5个)。我们把这个叫做1(5) shot 5 ways task. 大家可以看到,这就是你的训练集,一共5个(或者25个样本)。但是...
原因很简单,因为实验的setting其实是有一个uniform class prior. 具体来说就是每个类的测试集+训练集是相等的。OT这一套其实巧妙地利用了这个trick使得但凡基于OT的方法效果都差不多(比如PT-MAP和iLCT)。所以Realistic evaluation of transductive few-shot learning (NeurIPS 2021)这篇论文一定意义上指出了这个问题,...
Few-shot learning aims to build a learner that quickly generalizes to novel classes even when a limited number of labeled examples (so-called low-data problem) are available. Meta-learning is commonly deployed to mimic the test environment in a training phase for good generalization, where episod...
Few-shot image classification aims at learning a model from previous experiences that can be rapidly adapted to classify images of new classes with a few labeled examples. The learned model is easy to overfit since the distributions of new classes formed by a small number of samples are severely...
(Few-shot Detection)Review: Transductive Learning for zero-shot object detection )之间的数据分布并不匹配,导致了严重的域偏移问题; 由于模型是根据seen的数据训练的,导致引入了较大的偏差,在一般的zero-shot设定下,会把unseen数据更大概率的预测成seen里面的类别。 2. 提出方案 提出一个基于如何学习最优的组合...
The main challenge of cross-domain few-shot learning lies in the cross domain divergences in both the input data space and the output label space; 主要挑战在于输入数据空间和输出标签空间中的跨域差异; Work In this paper, we present a new method, Transductive Multi-Head Few-Shot learning (TMHFS...
Transductive Information Maximization for Few-Shot Learning https://arxiv.org/abs/2008.11297 (NeurIPS 2020) - MLDL/TIM
论文地址:http://papers.nips.cc/paper/5209-transfer-learning-in-a-transductive-setting.pdf。 该论文发表在NIPS2013,主要的工作是解决zero-shot learning或few-shot learning问题,即利用少量标签样本或者不用任何标签样本训练分类器,能够对未见过的类别进行分类。算法分为三个部分:(1)... ...
Code for ICLR19 paper: Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning. - csyanbin/TPN