“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
DeepXplore: Unleashing the Power of Automated Whitebox Testing for Deep Learning Systems
https://arxiv.org/abs/1705.06640
Authors: Kexin Pei, Yinzhi Cao , Junfeng Yang, Suman Jana
Introduction
DL systems often exhibit
EfficientNetV2: Smaller Models & Faster Training
#CNN #EfficientNet #model scaling #progressive learning
Model scaling
EfficientNet was first proposed in the original paper of Mingxing Tan &
Depth Map
A depth map is a heatmap of a picture, which indicates the distance relative to the camera. It contains fundamental
Unsupervised Learning in Spiking Neural Networks
Introduction
Spiking neural networks (SNNs) are computational models inspired by the biological behavior of neurons in the brain. These networks
Overview of Self Supervised Learning
Introduction
In this blog post, we’ll explore the concept of self-supervised learning, its potential for pushing AI toward human-level
SAM: Segment Anything Model by Meta AI
Segment Anything (SA) project tackles the three main questions for the arduous job of segmentation
https://segment-anything.com and https:
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
