Interpretable Medical Image Diagnosis with Explicd
As deep learning continues to revolutionize medical imaging, one critical question keeps surfacing: how can we trust what the model
Wavelet Diffusion Models are fast and scalable Image Generators
In today's blogpost, we will discuss about a recent paper published in the CVPR 2023 conference by VinAI
Face aging with Identity-preserved CGANs.
Paper : https://openaccess.thecvf.com/content_cvpr_2018/CameraReady/0430.pdf
Authors : Zongwei Wang, Xu Tang, Weixin Luo and Shenghua
“Vision Transformer Adapters for Generalizable Multitask Learning”
https://arxiv.org/pdf/2308.12372.pdf This paper has been presented at the ICCV'2023 Conference.
Authors : D.
A step into Machine Unlearning
#MachineUnlearning #Forgetting #DataPrivacy
What is Machine Unlearning ?
Basically, this concept represents the opposite of machine learning : it serves to make
“Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture”, the latest paper from Yann Lecun’s team at Meta
#ComputerVision #Self-SupervisedLearning #SSL #RepresentationLearning #I-JEPA
Introduction
I-JEPA [1], the latest self-supervised model from Meta AI, has been officially released: the
Introduction aux Graphes Neural Networks
Les graphes sont présents partout autour de nous et servent à représenter des connexions (appelées edges) entre des objets (appelés