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Found 12 papers, 2 papers with code
Unsupervised Domain Adaptation within Deep Foundation Latent Spaces
Through quantitative analysis, as well as qualitative interpretations of decision making, we demonstrate that the suggested method can improve upon existing baselines, as well as showcase the limitations of such approach yet to be solved.
Towards interpretable-by-design deep learning algorithms
The proposed framework named IDEAL (Interpretable-by-design DEep learning ALgorithms) recasts the standard supervised classification problem into a function of similarity to a set of prototypes derived from the training data, while taking advantage of existing latent spaces of large neural networks forming so-called Foundation Models (FM).
An Interpretable Deep Semantic Segmentation Method for Earth Observation
Earth observation is fundamental for a range of human activities including flood response as it offers vital information to decision makers.
Graph-Context Attention Networks for Size-Varied Deep Graph Matching
Deep learning for graph matching has received growing interest and developed rapidly in the past decade.
Ranked #4 on
Graph Matching
on PASCAL VOC
(matching accuracy metric)
Multi-Branch with Attention Network for Hand-Based Person Recognition
In this paper, we propose a novel hand-based person recognition method for the purpose of criminal investigations since the hand image is often the only available information in cases of serious crime such as sexual abuse.
Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning
Our proposed method, Global and Part-Aware Network (GPA-Net), creates global and local branches on the conv-layer for learning robust discriminative global and part-level features.
Deep Learning based Automated Forest Health Diagnosis from Aerial Images
Following the automated detection, we are able to automatically produce and calculate number of dead tree masks to label the dead trees in an image, as an indicator of forest health that could be linked to the causal analysis of environmental changes and the predictive likelihood of forest fire.
Towards Deep Machine Reasoning: a Prototype-based Deep Neural Network with Decision Tree Inference
In summary, we propose a new approach specifically advantageous for imbalanced multi-class problems that achieved two world records on well known hard benchmark problems and the best result on another problem in terms of accuracy.
A Novel Self-Organizing PID Approach for Controlling Mobile Robot Locomotion
A novel self-organizing fuzzy proportional-integral-derivative (SOF-PID) control system is proposed in this paper.
Towards Explainable Deep Neural Networks (xDNN)
In this paper, we propose an elegant solution that is directly addressing the bottlenecks of the traditional deep learning approaches and offers a clearly explainable internal architecture that can outperform the existing methods, requires very little computational resources (no need for GPUs) and short training times (in the order of seconds).
Novelty Detection and Learning from Extremely Weak Supervision
In this paper we offer a method and algorithm, which make possible fully autonomous (unsupervised) detection of new classes, and learning following a very parsimonious training priming (few labeled data samples only).
Fair-by-design explainable models for prediction of recidivism
Recidivism prediction provides decision makers with an assessment of the likelihood that a criminal defendant will reoffend that can be used in pre-trial decision-making.
