Matching Networks for One Shot Learning
Interesting way of doing one-shot learning with low-data by using an attention mechanism and a query to compare an image to other images for classification.
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Matching Networks for One Shot Learning arxiv.org
Classify a new example from a list of other examples (without definitive categories) and with low-data per classification task, but lots of data for lots of similar classification tasks - it seems better than siamese networks. To sum up: with Matching Networks, you can optimize…
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Prototypical Networks for Few-shot Learning arxiv.org
Use a distance metric in the loss to determine to which class does an object belongs to from a few examples.
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Attention Is All You Need arxiv.org
(AIAYN) - Introducing multi-head self-attention neural networks with positional encoding to do sentence-level NLP without any RNN nor CNN - this paper is a must-read (also see this explanation and this visualization of the paper).
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Effective Approaches to Attention-based Neural Machine Translation arxiv.org
Exploring different approaches to attention mechanisms.
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Slot Attention github.com
Reference implementation for Differentiable Patch Selection for Image Recognition.
Education › Papers > Attention Mechanisms: “Interesting way of doing one-shot learning with low-data by using an attention mechanism and a query to compare an image to other images for classification.”