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Found 7 papers, 4 papers with code
Maximally Compact and Separated Features with Regular Polytope Networks
Convolutional Neural Networks (CNNs) trained with the Softmax loss are widely used classification models for several vision tasks.
Fashion Recommendation Based on Style and Social Events
Overall, we integrate in a state of the art garment recommendation framework a style classifier and an event classifier in order to condition recommendation on a given query.
Regular Polytope Networks
Typically, a learnable transformation (i. e. the classifier) is placed at the end of such models returning a value for each class used for classification.
Garment Recommendation with Memory Augmented Neural Networks
In particular, we aim at retrieving a variety of modalities in which a certain garment can be combined.
Class-incremental Learning with Pre-allocated Fixed Classifiers
Contrarily to the standard expanding classifier, this allows: (a) the output nodes of future unseen classes to firstly see negative samples since the beginning of learning together with the positive samples that incrementally arrive; (b) to learn features that do not change their geometric configuration as novel classes are incorporated in the learning model.
Fix Your Features: Stationary and Maximally Discriminative Embeddings using Regular Polytope (Fixed Classifier) Networks
Typically, a learnable transformation (i. e. the classifier) is placed at the end of such models returning a value for each class used for classification.
Segmentation Free Object Discovery in Video
In this paper we present a simple yet effective approach to extend without supervision any object proposal from static images to videos.
