Abstract
In developing a few-shot classification model using deep networks, the limited number of samples in each class causes difficulty in utilizing statistical characteristics of the class distributions. In this paper, we propose a method to treat this difficulty by combining a probabilistic similarity based on intra-class statistics with a metric-based few-shot classification model. Noting that the probabilistic similarity estimated from intra-class statistics and the classifier of conventional few-shot classification models have a common assumption on the class distributions, we propose to apply the probabilistic similarity to obtain loss value for episodic learning of embedding network as well as to classify unseen test data. By defining the probabilistic similarity as the probability density of difference vectors between two samples with the same class label, it is possible to obtain a more reliable estimate of the similarity especially for the case of large number of classes. Through experiments on various benchmark data, we confirm that the probabilistic similarity can improve the classification performance, especially when the number of classes is large.
Highlights
We propose to combine the probabilistic similarity based on intra-class statistics [26,27,28,29] with the prototypical network [9] that is a representative few-shot classification model
Since the purpose pose of the experiments is to see the effect of the probabilistic similarity measure, we of the experiments is to see the effect of the probabilistic similarity measure, we mainly mainly compare its performance with the conventional model with Euclidean distance
We suggest a way of improving the distance-based classifier by using a probabilistic similarity, which is derived from a class-independent environmental factor estimated by using intra-class difference vectors
Summary
Pattern recognition methods using deep learning techniques have shown good results in many applications [1,2,3,4,5] These results can only be obtained with a sufficiently large number of training data. In few-shot learning for classification tasks, a classifier is required to recognize classes that are unseen in the learning phase, with a very limited number of samples. To achieve this goal, there have been proposed a number of few-shot classification models which are composed of two modules: an embedding module and a classification module [8,9,10,11,12].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
