arXiv:2601.21688v2 Announce Type: replace Abstract: Disentangled representation learning aims to map independent factors of variation to independent representation components. On one hand, purely unsupervised approaches have proven successful on fully disentangled synthetic data, but fail to recover semantic factors from real data without strong inductive biases. On the other hand, supervised approaches are unstable and hard to scale to large attribute sets because they rely on adversarial objectives or auxiliary classifiers. We introduce \textsc{XFactors}, a weakly-supervised VAE framework th
This paper represents continued academic progress in the fundamental AI task of disentangled representation learning, building on previous unsupervised and supervised methods.
Improved disentanglement in AI models allows for more robust, interpretable, and controllable AI systems, which is critical for their deployment in complex real-world applications.
This weakly-supervised VAE framework, XFACTORS, offers a more stable and scalable approach to disentangled representation learning, potentially overcoming limitations of prior methods.
- · AI researchers
- · Developers of interpretable AI systems
- · Industries requiring robust AI control
- · Developers of unstable adversarial disentanglement methods
More accurate and efficient AI models for tasks like image generation, anomaly detection, and decision-making.
Reduced need for extensive labeled datasets as models can better identify independent factors from less supervision.
Accelerated development of general-purpose AI and autonomous agents due to enhanced understanding and manipulation of latent factors.
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Read at arXiv cs.LG