A solid foundational understanding of XAI, primarily emphasizing how XAI methodologies can expose latent biases in datasets and reveal valuable insights.
-
Updated
Jun 6, 2024 - Jupyter Notebook
A solid foundational understanding of XAI, primarily emphasizing how XAI methodologies can expose latent biases in datasets and reveal valuable insights.
🤖 Segmentação de faixas de estrada utilizando o Segformer
HydraViT is a PyTorch implementation of the HydraViT model, an adaptive multi-branch transformer for multi-label disease classification from chest X-ray images. The repository provides the necessary code to train and evaluate the HydraViT model on the NIH Chest X-ray dataset.
This repository accompanies our paper Unlocking the Heart Using Adaptive Locked Agnostic Networks and enables replication of the key results.
Methodology used to classify face images based on unknown criteria as part of a datachallenge organised at Telecom Paris
A Survey on Transformer in CV.
Comparison of various deep learning-based medical imaging methods for diagnosing and classifying Alzheimer’s disease at different stages.
Multi Modal Task Oriented Dialogue System (MMTOD)
The repository contains supplementary material to my Master's thesis - Fine-grained Visual Recognition with Side Information
A modular Pytorch Implementation of ViTGAN
Using Visual Transformers to train a basic image classification model to classify images of lions, tigers, cheetahs, tigers and leopards
An easy and minimal implementation of the Visual Transformer (ViT) in PyTorch, from scratch!
Add a description, image, and links to the visual-transformers topic page so that developers can more easily learn about it.
To associate your repository with the visual-transformers topic, visit your repo's landing page and select "manage topics."