tomealbuquerque

CSSL

Code for the paper "Credal Self-Supervised Learning" by Julian Lienen and Eyke Hüllermeier (NeurIPS 2021).

dino

PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

EDoF-CNN

Extended depth of field methods using CNN's

embedded-regularization

eyewash

Hackathon Retail 4.0 by Galp @ TAIKAI

multimodal-glioma-biomarkers-detection

Multimodal Context-Aware Detection of Glioma Biomarkers using MRI and WSI

multimodal-lung-cancer-prediction

Model to predict Lung Cancer using multimodal tabular data - MICCAI Hackathon 2022

ordinal-datasets

A list of ordinal datasets publicly available

ordinal-losses

Work involving multiple losses for ordinal classification

Quasi-Unimodal-Loss

tomealbuquerque/EDoF-CNN

Extended depth of field methods using CNN's

tomealbuquerque/multimodal-glioma-biomarkers-detection

Multimodal Context-Aware Detection of Glioma Biomarkers using MRI and WSI

tomealbuquerque/ordinal-losses

Work involving multiple losses for ordinal classification

tomealbuquerque/ordinal-datasets

A list of ordinal datasets publicly available

tomealbuquerque/eyewash

Hackathon Retail 4.0 by Galp @ TAIKAI

tomealbuquerque/Quasi-Unimodal-Loss

tomealbuquerque/multimodal-lung-cancer-prediction

Model to predict Lung Cancer using multimodal tabular data - MICCAI Hackathon 2022

tomealbuquerque/CSSL

Code for the paper "Credal Self-Supervised Learning" by Julian Lienen and Eyke Hüllermeier (NeurIPS 2021).

tomealbuquerque/dino

PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

tomealbuquerque/embedded-regularization

tomealbuquerque/fixmatch

A simple method to perform semi-supervised learning with limited data.

tomealbuquerque/Tome-Mendes-Albuquerque

tomealbuquerque/tomealbuquerque