JEPA Architecture & Design

The JEPA (Joint-Embedding Predictive Architecture) framework provides a flexible, modular approach to self-supervised learning that can work with any encoder and predictor you design.

Core Philosophy

JEPA is built on the principle of architectural flexibility. Instead of forcing you into specific model designs, JEPA provides a framework that can accommodate:

Any encoder architecture (CNNs, Transformers, MLPs, custom designs)
Any predictor design (simple MLPs, complex networks, attention mechanisms)
Any data modality (images, text, time series, audio, multimodal)

Key Features

Modular Design

Pass any encoder and predictor to the JEPA model - no architectural constraints

Flexible Architecture

Works seamlessly with transformers, CNNs, MLPs, or your custom architectures

Easy to Extend

Create custom encoders and predictors by simply inheriting from nn.Module

Basic Usage

from models import JEPA, Encoder, Predictor

# Create encoder and predictor
encoder = Encoder(hidden_dim=256)
predictor = Predictor(hidden_dim=256)

# Create JEPA model
jepa_model = JEPA(encoder, predictor)

# Forward pass
pred, target = jepa_model(state_t, state_t1)
loss = jepa_model.loss(pred, target)

Custom Encoders and Predictors

The real power of JEPA comes from creating custom components tailored to your specific use case:

Custom CNN Encoder

import torch.nn as nn

class CustomCNNEncoder(nn.Module):
    def __init__(self, input_channels, hidden_dim):
        super().__init__()
        self.conv_net = nn.Sequential(
            nn.Conv2d(input_channels, 64, 3, padding=1),
            nn.ReLU(),
            nn.AdaptiveAvgPool2d((1, 1)),
            nn.Flatten(),
            nn.Linear(64, hidden_dim)
        )
    
    def forward(self, x):
        return self.conv_net(x)

Custom Predictor

class CustomPredictor(nn.Module):
    def __init__(self, hidden_dim):
        super().__init__()
        self.predictor = nn.Linear(hidden_dim, hidden_dim)
    
    def forward(self, x):
        return self.predictor(x)

# Use with JEPA
custom_encoder = CustomCNNEncoder(input_channels=3, hidden_dim=256)
custom_predictor = CustomPredictor(hidden_dim=256)
jepa_model = JEPA(custom_encoder, custom_predictor)

Architecture Components

BaseModel

Provides common functionality for all models:

Model persistence: Save and load trained models
Configuration management: Handle model hyperparameters
Device management: Automatic CPU/GPU handling
Utility methods: Common operations across models

JEPA Core

The main model class that orchestrates training:

Encoder-Predictor coordination: Manages the interaction between components
Loss computation: Computes contrastive or reconstruction losses
Forward pass handling: Manages data flow through the architecture
Gradient management: Handles backpropagation and optimization

Provided Implementations

JEPA comes with several ready-to-use components:

Encoders:

Encoder: Basic transformer encoder for sequential data
CNNEncoder: Convolutional encoder for spatial data
MLPEncoder: Simple feedforward encoder

Predictors:

Predictor: Simple MLP predictor
AttentionPredictor: Attention-based predictor
RecurrentPredictor: RNN-based predictor

Design Patterns

Encoder Design

Your encoder should:

Accept raw input data in your target modality
Output fixed-size embeddings that capture meaningful representations
Be differentiable throughout (standard PyTorch requirement)
Handle batching appropriately for your data type

class MyEncoder(nn.Module):
    def __init__(self, input_spec, hidden_dim):
        super().__init__()
        # Define your architecture here
        
    def forward(self, x):
        # x: input data in your format
        # returns: tensor of shape (batch_size, hidden_dim)
        return embeddings

Predictor Design

Your predictor should:

Accept context embeddings from the encoder
Predict target embeddings in the same space
Learn meaningful transformations that capture temporal/spatial relationships
Output embeddings of the same dimensionality as the encoder

class MyPredictor(nn.Module):
    def __init__(self, hidden_dim):
        super().__init__()
        # Define prediction architecture
        
    def forward(self, context_embedding):
        # context_embedding: encoder output for context data
        # returns: predicted embedding for target data
        return predicted_embedding

Advanced Usage Patterns

Multi-Scale Encoders

class MultiScaleEncoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.local_encoder = LocalCNNEncoder()
        self.global_encoder = GlobalTransformerEncoder()
        self.fusion = FusionLayer()
        
    def forward(self, x):
        local_features = self.local_encoder(x)
        global_features = self.global_encoder(x)
        return self.fusion(local_features, global_features)

Hierarchical Predictors

class HierarchicalPredictor(nn.Module):
    def __init__(self):
        super().__init__()
        self.coarse_predictor = CoarsePredictor()
        self.fine_predictor = FinePredictor()
        
    def forward(self, context):
        coarse_pred = self.coarse_predictor(context)
        fine_pred = self.fine_predictor(context, coarse_pred)
        return fine_pred

Best Practices

Model Design

Start simple: Begin with basic architectures and gradually add complexity
Match architecture to data: Use CNNs for spatial data, RNNs for sequential data
Consider computational constraints: Balance model size with available resources
Use pretrained components: Leverage existing pretrained encoders when possible

Training Strategies

Progressive training: Start with smaller models and scale up
Curriculum learning: Begin with easier examples and increase difficulty
Regularization: Use dropout, weight decay, and other regularization techniques
Monitoring: Track both encoder and predictor performance separately

Performance Optimization

Batch size tuning: Find the optimal batch size for your hardware
Mixed precision: Use automatic mixed precision for faster training
Gradient accumulation: Simulate larger batch sizes when memory constrained
Efficient data loading: Optimize your data pipeline for maximum throughput

Examples and Use Cases

For complete examples showing these concepts in action, see:

Vision Examples - CNN encoders for image processing
NLP Examples - Transformer encoders for text
Time Series Examples - Temporal encoders for sequential data
Multimodal Examples - Cross-modal architectures

The flexibility of JEPA means you can adapt it to virtually any domain by designing appropriate encoders and predictors for your specific use case.