Add to ORKGPrototypical network for Few shot learning tries to learn an embedding function in the encoder that embeds images with similar features close to one another in the embedding space. However, in this process, the support set samples for a task are embedded independently of one other, and hence, the inter-class closeness is not taken into account. Thus, in the presence of similar-looking classes in a task, the embeddings will tend to be close to each other in the embedding space and even possibly overlap in some regions, which is not desirable for classification. In this paper, we propose an approach that intuitively pushes the embeddings of each of the classes away from the others in the meta-testing phase, thereby grouping them closely based...
It is assumed that pre-training provides the feature extractor with strong class transferability and...
Modern deep learning requires large-scale extensively labelled datasets for training. Few-shot learn...
Meta-learning has been shown to be an effective strategy for few-shot learning. The key idea is to l...
Few-shot learning focuses on learning a new visual concept with very limited labelled examples. A su...
In few-shot classification, we are interested in learning algorithms that train a classifier from on...
The prototypical network is a prototype classifier based on meta-learning and is widely used for few...
Added experiments with different network architectures and input image resolutionsInternational audi...
Few-shot classification aims to adapt to new tasks with limited labeled examples. To fully use the a...
© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. A variety of...
One of the fundamental problems in machine learning is training high-quality neural network models u...
The focus of recent few-shot learning research has been on the development of learning methods that ...
Few-shot learning is a challenging task that aims at training a classifier for unseen classes with o...
We propose regression networks for the problem of few-shot classification, where a classifier must g...
We propose a novel framework for few-shot learning by leveraging large-scale vision-language models ...
Despite recent success of deep neural networks, it remains challenging to efficiently learn new visu...
It is assumed that pre-training provides the feature extractor with strong class transferability and...
Modern deep learning requires large-scale extensively labelled datasets for training. Few-shot learn...
Meta-learning has been shown to be an effective strategy for few-shot learning. The key idea is to l...
Few-shot learning focuses on learning a new visual concept with very limited labelled examples. A su...
In few-shot classification, we are interested in learning algorithms that train a classifier from on...
The prototypical network is a prototype classifier based on meta-learning and is widely used for few...
Added experiments with different network architectures and input image resolutionsInternational audi...
Few-shot classification aims to adapt to new tasks with limited labeled examples. To fully use the a...
© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. A variety of...
One of the fundamental problems in machine learning is training high-quality neural network models u...
The focus of recent few-shot learning research has been on the development of learning methods that ...
Few-shot learning is a challenging task that aims at training a classifier for unseen classes with o...
We propose regression networks for the problem of few-shot classification, where a classifier must g...
We propose a novel framework for few-shot learning by leveraging large-scale vision-language models ...
Despite recent success of deep neural networks, it remains challenging to efficiently learn new visu...
It is assumed that pre-training provides the feature extractor with strong class transferability and...
Modern deep learning requires large-scale extensively labelled datasets for training. Few-shot learn...
Meta-learning has been shown to be an effective strategy for few-shot learning. The key idea is to l...