Assessing two novel distance-based loss functions for few-shot image classification
Few-shot learning is a challenging area of research which aims to learn new concepts with only a few labeled samples of data. Recent works based on metric-learning approaches benefit from the meta-learning process in which we have episodic tasks conformed by support set (training) and query set (test), and the objective is to learn a similarity comparison metric between those sets. Due to the lack of data, the learning process of the embedding network becomes an important part of the few-shot task. In this work, we propose two different loss functions which consider the importance of the embedding vectors by looking at the intra-class and inter-class distance between the few data. The first loss function is the Proto-Triplet Loss, which is based on the original triplet loss with the modifications needed to better work on few-shot scenarios. The second loss function is based on an inter and intra class nearest neighbors score, which help us to know the quality of embeddings obtained from the trained network. Extensive experimental results on the miniImagenNet benchmark increase the accuracy performance from other metric-based few-shot learning methods by a margin of $2\%$, demonstrating the capability of these loss functions to allow the network to generalize better to previously unseen classes.
Code (0)
등록된 구현이 없습니다.
Tasks
Few-Shot Image ClassificationFew-Shot Learningimage-classificationImage ClassificationMeta-LearningMetric LearningTripletMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification
Few-shot learning is a challenging area of research that aims to learn new concepts with only a few labeled samples of data. Recent works based on metric-learning approaches leverage the meta-learning approach, which is …
AllFew-Shot Image ClassificationFew-Shot Learningimage-classification+4Revisiting Distance Metric Learning for Few-Shot Natural Language Classification
Distance Metric Learning (DML) has attracted much attention in image processing in recent years. This paper analyzes its impact on supervised fine-tuning language models for Natural Language Processing (NLP) classificati…
Few-Shot LearningLanguage ModelingLanguage ModellingMetric LearningDistance-Aware eXplanation Based Learning
eXplanation Based Learning (XBL) is an interactive learning approach that provides a transparent method of training deep learning models by interacting with their explanations. XBL augments loss functions to penalize a m…
image-classificationImage ClassificationClass Distance Weighted Cross Entropy Loss for Classification of Disease Severity
Assessing disease severity with ordinal classes, where each class reflects increasing severity levels, benefits from loss functions designed for this ordinal structure. Traditional categorical loss functions, like Cross-…
image-classificationImage ClassificationDistance Metric Learning Loss Functions in Few-Shot Scenarios of Supervised Language Models Fine-Tuning
This paper presents an analysis regarding an influence of the Distance Metric Learning (DML) loss functions on the supervised fine-tuning of the language models for classification tasks. We experimented with known datase…
Metric Learning