Name
Abstract | ||
|---|---|---|
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. Prototypical Networks learn a metric space in which classification can be performed by computing distances to prototype representations of each class. Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning. We further extend Prototypical Networks to zero-shot learning and achieve state-of-the-art results on the CU-Birds dataset. |
Year | Venue | DocType |
|---|---|---|
2017 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017) | Conference |
Volume | ISSN | Citations |
30 | 1049-5258 | 196 |
PageRank | References | Authors |
4.09 | 19 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Snell, Jake | 1 | 222 | 6.86 |
| Kevin Swersky | 2 | 1118 | 52.13 |
| Richard S. Zemel | 3 | 4958 | 425.68 |