Encoder Neural Network Explanation

What I worked on

Clarified how encoders compress input features into lower-dimensional representations and how this connects to JEPA architectures.

What I noticed

• Encoders reduce dimensionality through learned compression
• JEPA uses a multi-block masking approach for context and target blocks

”Aha” Moment

Encoders aren’t just compression layers. They create representations that preserve structure useful for prediction.

What still feels messy

Still figuring out best practices for block-based masking in JEPA.

Next step

Prototype a simple encoder-decoder pipeline using multi-block masking.