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Found 25 papers, 13 papers with code
Processing of missing data by neural networks
We propose a general, theoretically justified mechanism for processing missing data by 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.
Divide and not forget: Ensemble of selectively trained experts in Continual Learning
Class-incremental learning is becoming more popular as it helps models widen their applicability while not forgetting what they already know.
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.
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.
Persistence Bag-of-Words for Topological Data Analysis
Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs).
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.
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.
Augmentation-aware Self-supervised Learning with Conditioned Projector
By learning to remain invariant to applied data augmentations, methods such as SimCLR and MoCo are able to reach quality on par with supervised approaches.
Cascade context encoder for improved inpainting
For this purpose, we train context encoder for 64x64 pixels images in a standard way and use its resized output to fill in the missing input region of the 128x128 context encoder, both in training and evaluation phase.
Topological descriptors for 3D surface analysis
We investigate topological descriptors for 3D surface analysis, i. e. the classification of surfaces according to their geometric fine structure.
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.
Deep learning approach to describe and classify fungi microscopic images
Preliminary diagnosis of fungal infections can rely on microscopic examination.
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.
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.
Visual Probing: Cognitive Framework for Explaining Self-Supervised Image Representations
Motivated by this observation, we introduce a novel visual probing framework for explaining the self-supervised models by leveraging probing tasks employed previously in natural language processing.
SONG: Self-Organizing Neural Graphs
Recent years have seen a surge in research on deep interpretable neural networks with decision trees as one of the most commonly incorporated tools.
ProPaLL: Probabilistic Partial Label Learning
Partial label learning is a type of weakly supervised learning, where each training instance corresponds to a set of candidate labels, among which only one is true.
Active Visual Exploration Based on Attention-Map Entropy
Active visual exploration addresses the issue of limited sensor capabilities in real-world scenarios, where successive observations are actively chosen based on the environment.
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.
Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations
Prototypical parts-based networks are becoming increasingly popular due to their faithful self-explanations.
Beyond Grids: Exploring Elastic Input Sampling for Vision Transformers
Vision transformers have excelled in various computer vision tasks but mostly rely on rigid input sampling using a fixed-size grid of patches.
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.
ProPML: Probability Partial Multi-label Learning
Partial Multi-label Learning (PML) is a type of weakly supervised learning where each training instance corresponds to a set of candidate labels, among which only some are true.
AdaGlimpse: Active Visual Exploration with Arbitrary Glimpse Position and Scale
Active Visual Exploration (AVE) is a task that involves dynamically selecting observations (glimpses), which is critical to facilitate comprehension and navigation within an environment.
