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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.
#7 best model for Few-Shot Image Classification on OMNIGLOT - 1-Shot Learning
Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network.
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
We define Open Long-Tailed Recognition (OLTR) as learning from such naturally distributed data and optimizing the classification accuracy over a balanced test set which include head, tail, and open classes.
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.
#6 best model for Few-Shot Image Classification on OMNIGLOT - 1-Shot Learning
We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not.