Comparison of Embedded Spaces for Deep Learning Classification
Embedded spaces are a key feature in deep learning. Good embedded spaces represent the data well to support classification and advanced techniques such as open-set recognition, few-short learning and explainability. This paper presents a compact overview of different techniques to design embedded spaces for classification. It compares different loss functions and constraints on the network parameters with respect to the achievable geometric structure of the embedded space. The techniques are demonstrated with two and three-dimensional embeddings for the MNIST, Fashion MNIST and CIFAR-10 datasets, allowing visual inspection of the embedded spaces.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationDeep LearningOpen Set LearningSimilar Papers 제목 키워드 기반
Performance of Gaussian Mixture Model Classifiers on Embedded Feature Spaces
Data embeddings with CLIP and ImageBind provide powerful features for the analysis of multimedia and/or multimodal data. We assess their performance here for classification using a Gaussian Mixture models (GMMs) based la…
Image ClassificationDiscriminatively Embedded K-Means for Multi-View Clustering
In real world applications, more and more data, for example, image/video data, are high dimensional and represented by multiple views which describe different perspectives of the data. Efficiently clustering such data is…
ClusteringEmbedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples
Embeddings mapping high-dimensional discrete input to lower-dimensional continuous vector spaces have been widely adopted in machine learning applications as a way to capture domain semantics. Interviewing 13 embedding u…
Complex Networks for Pattern-Based Data Classification
Data classification techniques partition the data or feature space into smaller sub-spaces, each corresponding to a specific class. To classify into subspaces, physical features e.g., distance and distributions are utili…
ClassificationFeature-based morphological analysis of shape graph data
This paper introduces and demonstrates a computational pipeline for the statistical analysis of shape graph datasets, namely geometric networks embedded in 2D or 3D spaces. Unlike traditional abstract graphs, our purpose…