JEPA Framework Documentation

Welcome to the JEPA (Joint-Embedding Predictive Architecture) framework documentation. JEPA is a powerful self-supervised learning framework that learns representations by predicting parts of the input from other parts.

🚀 Quick Start

import torch
from torch.utils.data import DataLoader, TensorDataset

from jepa.models import JEPA
from jepa.models.encoder import Encoder
from jepa.models.predictor import Predictor
from jepa.trainer import create_trainer

dataset = TensorDataset(torch.randn(64, 16, 128), torch.randn(64, 16, 128))
train_loader = DataLoader(dataset, batch_size=8, shuffle=True)

encoder = Encoder(hidden_dim=128)
predictor = Predictor(hidden_dim=128)
model = JEPA(encoder=encoder, predictor=predictor)

trainer = create_trainer(model)
trainer.train(train_loader, num_epochs=1)

Or use the CLI:

jepa-train --config config/default_config.yaml

Action-Conditioned JEPA

If actions influence transitions, use JEPAAction to condition predictions on actions:

from jepa import JEPAAction
import torch.nn as nn

state_encoder = ...  # outputs state_dim
action_encoder = ... # outputs action_dim
predictor = nn.Sequential(nn.Linear(state_dim + action_dim, state_dim))
model = JEPAAction(state_encoder, action_encoder, predictor)

📖 Documentation Sections

User Guide

Core Concepts

API Reference

Examples

🎯 Key Features

🔧 Modular Design

  • Flexible encoder-predictor architecture

  • Support for any PyTorch model as encoder/predictor

  • Easy to extend and customize

🌍 Multi-Modal Support

  • Computer Vision (images, videos)

  • Natural Language Processing (text)

  • Time Series (sequential data)

  • Audio processing

  • Multimodal learning

⚡ High Performance

  • Mixed precision training

  • Distributed training support

  • Triton kernel optimization

  • Memory-efficient implementations

📊 Comprehensive Logging

  • Weights & Biases integration

  • TensorBoard support

  • Console logging

  • Multi-backend logging system

🎛️ Production Ready

  • CLI interface for easy deployment

  • Flexible configuration system

  • Comprehensive testing

  • Docker support

🏗️ Architecture Overview

JEPA follows a simple yet powerful architecture:

Input Data → [Context/Target Split] → Encoder → Joint Embedding Space
                                         ↓
Target Embedding ← Predictor ← Context Embedding

The model learns by:

  1. Splitting input into context and target regions

  2. Encoding both context and target separately

  3. Predicting target embeddings from context embeddings

  4. Learning representations that capture meaningful relationships

🎨 Use Cases

Computer Vision

  • Image classification pretraining

  • Object detection backbone

  • Medical image analysis

  • Satellite imagery processing

Natural Language Processing

  • Language model pretraining

  • Document understanding

  • Code representation learning

  • Cross-lingual embeddings

Time Series

  • Forecasting model pretraining

  • Anomaly detection

  • Financial data analysis

  • IoT sensor data processing

Multimodal Learning

  • Vision-language models

  • Audio-visual learning

  • Cross-modal retrieval

  • Multimodal reasoning

📄 Citation

If you use JEPA in your research, please cite:

@article{jepa2024,
  title={Joint-Embedding Predictive Architecture for Self-Supervised Learning},
  author={Dilip Venkatesh},
  year={2025}
}

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

Built with ❤️ by the Dilip Venkatesh