This is the Linux app named JEPA whose latest release can be downloaded as jepasourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named JEPA with OnWorks for free.
Befolgen Sie diese Anweisungen, um diese App auszuführen:
- 1. Diese Anwendung auf Ihren PC heruntergeladen.
- 2. Geben Sie in unserem Dateimanager https://www.onworks.net/myfiles.php?username=XXXXX den gewünschten Benutzernamen ein.
- 3. Laden Sie diese Anwendung in einem solchen Dateimanager hoch.
- 4. Starten Sie den OnWorks Linux-Online- oder Windows-Online-Emulator oder den MACOS-Online-Emulator von dieser Website.
- 5. Rufen Sie vom gerade gestarteten OnWorks Linux-Betriebssystem aus unseren Dateimanager https://www.onworks.net/myfiles.php?username=XXXXX mit dem gewünschten Benutzernamen auf.
- 6. Laden Sie die Anwendung herunter, installieren Sie sie und führen Sie sie aus.
SCREENSHOTS
Ad
JEPA
BESCHREIBUNG
JEPA (Joint-Embedding Predictive Architecture) captures the idea of predicting missing high-level representations rather than reconstructing pixels, aiming for robust, scalable self-supervised learning. A context encoder ingests visible regions and predicts target embeddings for masked regions produced by a separate target encoder, avoiding low-level reconstruction losses that can overfit to texture. This makes learning focus on semantics and structure, yielding features that transfer well with simple linear probes and minimal fine-tuning. The repository provides training recipes, data pipelines, and evaluation utilities for image JEPA variants and often includes ablations that illuminate which masking and architectural choices matter. Because the objective is non-autoregressive and operates in embedding space, JEPA tends to be compute-efficient and stable at scale. The approach has become a strong alternative to contrastive or pixel-reconstruction methods for representation learning.
Eigenschaften
- Predictive learning in embedding space instead of pixel reconstruction
- Separate context and target encoders with masked-region objectives
- Strong linear-probe and low-shot transfer performance
- Stable, efficient training without heavy negative sampling
- Clear recipes and ablations for masking and architecture choices
- Modular code for extending to new modalities or datasets
Programmiersprache
Python
Kategorien
This is an application that can also be fetched from https://sourceforge.net/projects/jepa.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.