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.
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
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).
#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.
#6 best model for Few-Shot Image Classification on Mini-ImageNet - 5-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.
These low-shot learning frameworks will reduce the manual image annotation burden and improve semantic segmentation performance for remote sensing imagery.
In this paper we aim at facilitating generalization for deep networks while supporting interpretability of the learned representations.
#3 best model for Few-Shot Image Classification on CUB-200 - 0-Shot Learning