arXiv:2605.20302v1 Announce Type: new Abstract: Supervised classification has a theoretical optimum, Neural Collapse (NC), yet neither of its two dominant paradigms reaches it in practice. Cross entropy (CE) leaves radial degrees of freedom unconstrained and converges to a degenerate geometry, while supervised contrastive learning (SCL) drives features toward NC during pretraining but discards this structure in a post hoc linear probing phase. We show that both paradigms are different appearances of the same method, prototype contrast on the unit hypersphere, and that closing the gap requires
This research provides a theoretical advancement in understanding and optimizing supervised classification, directly addressing current practical limitations in AI model training.
Improved classification efficiency and theoretical understanding can lead to more robust and higher-performing AI systems, impacting various applications from computer vision to autonomous agents.
Current methods like cross-entropy and supervised contrastive learning are now viewed as different expressions of a unified underlying problem, guiding future research toward more optimal solutions.
- · AI researchers
- · Machine learning developers
- · Sectors using computer vision
- · AI-driven product companies
- · Developers reliant on suboptimal classification methods
More efficient and accurate deep learning models will be developed.
This improved accuracy can accelerate the development and deployment of sophisticated AI applications, including autonomous systems.
Enhanced AI capabilities could further consolidate power among leading AI research institutions and tech giants, increasing their lead in AI innovation.
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Read at arXiv cs.LG