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BiJEPA: Bi-directional Joint Embedding Predictive Architecture for Symmetric Representation Learning

arXiv:2603.00049v1 Announce Type: new Abstract: Self-Supervised Learning (SSL) has shifted from pixel-level reconstruction to latent space prediction, spearheaded by the Joint Embedding Predictive Architecture (JEPA). While effective, standard JEPA models typically rely on a uni-directional prediction mechanism (e.g. Context $\to$ Target), potentially neglecting the informative signal inherent in the inverse relationship, degrading its performance. In this work, we propose \textbf{BiJEPA}, a \textit{Bi-Directional Joint Embedding Predictive Architecture} that enforces cycle-consistent predictability between data segments. We address the inherent instability of symmetric prediction (representation explosion) by introducing a critical norm regularization mechanism on the representation vectors. We evaluate BiJEPA on three distinct modalities: synthetic periodic signals, chaotic Lorenz attractor trajectories, and high-dimensional image data (MNIST). Our results demonstrate that BiJEPA ac

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Yongchao Huang · · 1 min read · 16 views
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arXiv:2603.00049v1 Announce Type: new Abstract: Self-Supervised Learning (SSL) has shifted from pixel-level reconstruction to latent space prediction, spearheaded by the Joint Embedding Predictive Architecture (JEPA). While effective, standard JEPA models typically rely on a uni-directional prediction mechanism (e.g. Context $\to$ Target), potentially neglecting the informative signal inherent in the inverse relationship, degrading its performance. In this work, we propose \textbf{BiJEPA}, a \textit{Bi-Directional Joint Embedding Predictive Architecture} that enforces cycle-consistent predictability between data segments. We address the inherent instability of symmetric prediction (representation explosion) by introducing a critical norm regularization mechanism on the representation vectors. We evaluate BiJEPA on three distinct modalities: synthetic periodic signals, chaotic Lorenz attractor trajectories, and high-dimensional image data (MNIST). Our results demonstrate that BiJEPA achieves stable convergence without collapse, captures the semantic structure of chaotic systems, and learns robust temporal and spatial representations capable of generation and generalisation, offering a more holistic approach to representation learning.

Executive Summary

The article proposes BiJEPA, a Bi-Directional Joint Embedding Predictive Architecture that offers a more holistic approach to representation learning. By introducing a norm regularization mechanism, BiJEPA addresses the instability of symmetric prediction, enabling stable convergence and capturing the semantic structure of chaotic systems. The model demonstrates robust temporal and spatial representations, capable of generation and generalization, across three distinct modalities. The results indicate that BiJEPA outperforms standard JEPA models, offering a significant improvement in representation learning. The proposed architecture has the potential to revolutionize self-supervised learning and has far-reaching implications for various applications, including image and signal processing, and data analysis.

Key Points

  • ▸ BiJEPA introduces a bi-directional prediction mechanism to leverage the informative signal inherent in the inverse relationship.
  • ▸ A norm regularization mechanism is proposed to address the instability of symmetric prediction.
  • ▸ BiJEPA demonstrates robust temporal and spatial representations across three distinct modalities.

Merits

Strength in Addressing Instability

BiJEPA's norm regularization mechanism effectively addresses the instability of symmetric prediction, enabling stable convergence and improving representation learning.

Improved Representation Learning

BiJEPA outperforms standard JEPA models, demonstrating robust temporal and spatial representations and capturing the semantic structure of chaotic systems.

Holistic Approach to Representation Learning

BiJEPA offers a more holistic approach to representation learning, enabling the capturing of complex relationships between data segments and improving generalization.

Demerits

Computational Complexity

BiJEPA's bi-directional prediction mechanism and norm regularization mechanism may increase computational complexity, potentially limiting its application in resource-constrained environments.

Limited Evaluation

The evaluation of BiJEPA is limited to three distinct modalities, and its performance on other modalities and applications remains to be explored.

Expert Commentary

BiJEPA's introduction of a bi-directional prediction mechanism and norm regularization mechanism addresses the limitations of standard JEPA models, offering a more holistic approach to representation learning. The proposed architecture has the potential to revolutionize self-supervised learning and has far-reaching implications for various applications. However, its computational complexity and limited evaluation may pose challenges to its adoption. Further research is necessary to explore its performance on other modalities and applications, as well as its scalability and robustness.

Recommendations

  • ✓ Further research is necessary to explore BiJEPA's performance on other modalities and applications, as well as its scalability and robustness.
  • ✓ The development of more efficient and scalable algorithms for BiJEPA is necessary to overcome its computational complexity.

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