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Ijepa Vith16 1k

Developed by facebook
I-JEPA is a self-supervised learning method that predicts representations of other parts of an image from partial representations, without relying on predefined manual data transformations or pixel-level detail filling.
Downloads 153
Release Time : 8/26/2024

Model Overview

I-JEPA employs a latent space predictor as a foundational world model, capable of modeling spatial uncertainty in static images from partially observable contexts, focusing on predicting high-level information rather than pixel-level details.

Model Features

Self-supervised learning
Does not rely on predefined manual data transformation invariance, avoiding bias towards specific downstream tasks
Latent space prediction
Uses a latent space predictor instead of a pixel decoder, focusing on high-level semantic information rather than pixel-level details
World model
Can serve as a foundational world model, modeling spatial uncertainty in static images from partially observable contexts

Model Capabilities

Image feature extraction
Semantic representation learning

Use Cases

Computer Vision
Image classification
Use extracted features for image classification tasks
Feature extraction
Extract high-level semantic features from images for downstream tasks
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