JEPA Model (Joint Embedding Predictive Architecture)
This repository contains an experimental implementation of a Joint Embedding Predictive Architecture (JEPA)–style model for learning representations without explicit reconstruction or contrastive objectives.
The core idea is simple: predict representations, not pixels.
Overview
JEPA is a self-supervised learning paradigm where a model learns by predicting the latent representation of a target signal from a context signal, rather than reconstructing raw inputs.
This repository explores:
Representation prediction in latent space
Decoupling prediction from reconstruction loss
A minimal, inspectable JEPA-style training loop
The implementation is intentionally lightweight to make the learning dynamics easy to study, modify, and extend.
Key Concepts
Context Encoder Encodes partial or masked input into a latent representation.
Target Encoder Encodes the full or target signal into a latent representation (often stop-gradient or EMA-based in advanced variants).
Predictor Network Maps context embeddings to predicted target embeddings.
Objective Minimize distance between predicted and target embeddings in latent space.
No pixel-level reconstruction. No contrastive negative sampling.
Installation
Clone the repository:
git clone https://github.com/deepspace28/JEPA_MODEL.git cd JEPA_MODEL
Install dependencies:
pip install -r requirements.txt
Recommended: use a virtual environment.
Usage
Run the training script (example):
python src/train.py
You can modify:
model depth and embedding size
masking strategy
loss function
optimizer and learning rate
to experiment with different JEPA-style behaviors.
Why JEPA?
Traditional self-supervised methods often rely on:
reconstruction losses (autoencoders), or
contrastive losses with large batch sizes.
JEPA-style models aim to:
learn semantic structure directly in latent space
reduce dependence on heavy augmentation or negatives
scale more naturally to complex modalities
This repository is a research sandbox, not a production framework.
Status
Experimental
Research-focused
Actively iterated
Expect breaking changes.