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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.
#2 best model for Few-Shot Image Classification on OMNIGLOT - 5-Shot, 5-way
We conclude with a discussion of the rapid learning vs feature reuse question for meta-learning algorithms more broadly.
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.
#3 best model for Image Classification on Tiered ImageNet 5-way (5-shot)
In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is crucial.
#2 best model for Few-Shot Image Classification on Mini-Imagenet 20-way (1-shot)
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.
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.
#2 best model for Image Classification on Tiered ImageNet 5-way (5-shot)
Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples.
#2 best model for Few-Shot Image Classification on Mini-ImageNet-CUB 5-way (5-shot)
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.
#2 best model for Few-Shot Image Classification on Mini-ImageNet-CUB 5-way (1-shot)
We conduct detailed analysis of the main components that lead to high transfer performance.
SOTA for Image Classification on ObjectNet (Bounding Box) (using extra training data)