Few-shot image classification is the task of doing image classification with only a few examples for each category (typically < 6 examples).
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We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning.
This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i. e., learns quickly) when presented with a previously unseen task sampled from this distribution.
#5 best model for Few-Shot Image Classification on OMNIGLOT - 1-Shot Learning
We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class.
Few-shot classiﬁcation aims to learn a classiﬁer to recognize unseen classes during training with limited labeled examples.
#3 best model for Few-Shot Image Classification on Mini-ImageNet - 5-Shot Learning
Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples.
#3 best model for Few-Shot Image Classification on CUB 200 5-way 5-shot
In this context, the goal of our work is to devise a few-shot visual learning system that during test time it will be able to efficiently learn novel categories from only a few training data while at the same time it will not forget the initial categories on which it was trained (here called base categories).
#4 best model for Few-Shot Image Classification on Mini-ImageNet - 1-Shot Learning
Our algorithm improves one-shot accuracy on ImageNet from 87. 6% to 93. 2% and from 88. 0% to 93. 8% on Omniglot compared to competing approaches.
#4 best model for Few-Shot Image Classification on OMNIGLOT - 1-Shot Learning
We propose to use these predictors as base learners to learn representations for few-shot learning and show they offer better tradeoffs between feature size and performance across a range of few-shot recognition benchmarks.
In this paper we propose a novel few-shot learning method called meta-transfer learning (MTL) which learns to adapt a deep NN for few shot learning tasks.