One-shot learning is the task of learning information about object categories from a single training example.
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.
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.
Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of "one-shot learning."
The process of learning good features for machine learning applications can be very computationally expensive and may prove difficult in cases where little data is available.
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).
#4 best model for Few-Shot Image Classification on Mini-ImageNet - 1-Shot Learning
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 order to create a personalized talking head model, these works require training on a large dataset of images of a single person.
We demonstrate empirical results on MS Coco highlighting challenges of the one-shot setting: while transferring knowledge about instance segmentation to novel object categories works very well, targeting the detection network towards the reference category appears to be more difficult.
SOTA for One-Shot Object Detection on COCO