Pip-net: Patch-based intuitive prototypes for interpretable image classification
M Nauta, J Schlötterer… - Proceedings of the …, 2023 - openaccess.thecvf.com
Interpretable methods based on prototypical patches recognize various components in an
image in order to explain their reasoning to humans. However, existing prototype-based …
image in order to explain their reasoning to humans. However, existing prototype-based …
Self-supervised learning of contextualized local visual embeddings
Abstract We present Contextualized Local Visual Embeddings (CLoVE), a self-supervised
convolutional-based method that learns representations suited for dense prediction tasks …
convolutional-based method that learns representations suited for dense prediction tasks …
Learning from memory: Non-parametric memory augmented self-supervised learning of visual features
This paper introduces a novel approach to improving the training stability of self-supervised
learning (SSL) methods by leveraging a non-parametric memory of seen concepts. The …
learning (SSL) methods by leveraging a non-parametric memory of seen concepts. The …
Scalable and Robust Transformer Decoders for Interpretable Image Classification with Foundation Models
Interpretable computer vision models can produce transparent predictions, where the
features of an image are compared with prototypes from a training dataset and the similarity …
features of an image are compared with prototypes from a training dataset and the similarity …
Representation learning via consistent assignment of views over random partitions
We present Consistent Assignment of Views over Random Partitions (CARP), a self-
supervised clustering method for representation learning of visual features. CARP learns …
supervised clustering method for representation learning of visual features. CARP learns …