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Found 13 papers, 6 papers with code
Token Recycling for Efficient Sequential Inference with Vision Transformers
To reduce this computational inefficiency, we introduce the TOken REcycling (TORE) modification for the ViT inference, which can be used with any architecture.
Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations
Prototypical parts-based networks are becoming increasingly popular due to their faithful self-explanations.
ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging
The most common MIL model is when we consider a bag as positive if at least one of its instances has a positive label.
ICICLE: Interpretable Class Incremental Continual Learning
Continual learning enables incremental learning of new tasks without forgetting those previously learned, resulting in positive knowledge transfer that can enhance performance on both new and old tasks.
ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts
We introduce ProtoSeg, a novel model for interpretable semantic image segmentation, which constructs its predictions using similar patches from the training set.
ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction
In this work, we propose the novel Prototypical Graph Regression Self-explainable Trees (ProGReST) model, which combines prototype learning, soft decision trees, and Graph Neural Networks.
Interpretable Image Classification with Differentiable Prototypes Assignment
We introduce ProtoPool, an interpretable image classification model with a pool of prototypes shared by the classes.
ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification
Multiple Instance Learning (MIL) gains popularity in many real-life machine learning applications due to its weakly supervised nature.
Classifying bacteria clones using attention-based deep multiple instance learning interpreted by persistence homology
In this work, we analyze if it is possible to distinguish between different clones of the same bacteria species (Klebsiella pneumoniae) based only on microscopic images.
ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discovery
In this paper, we introduce ProtoPShare, a self-explained method that incorporates the paradigm of prototypical parts to explain its predictions.
Kernel Self-Attention in Deep Multiple Instance Learning
There have been several attempts to create a model working with a bag of instances, however, they are assuming that there are no dependencies within the bag and the label is connected to at least one instance.
Deep learning approach to describe and classify fungi microscopic images
Preliminary diagnosis of fungal infections can rely on microscopic examination.
Deep learning approach to description and classification of fungi microscopic images
Diagnosis of fungal infections can rely on microscopic examination, however, in many cases, it does not allow unambiguous identification of the species due to their visual similarity.
