arXiv:2605.23819v1 Announce Type: cross Abstract: A central question in computational vision is whether human-like visual representations are better explained by discriminative or generative learning. Existing comparisons, however, often confound the learning objective with architecture, scale, and training data, leaving open whether the objective itself drives alignment. We address this confound using Joint Energy-Based Models (JEMs), which interpolate continuously between discriminative and generative training within a fixed architecture. By varying a single mixing coefficient, we isolate th
This research addresses a foundational question in AI, driven by the rapid advancements and widespread application of both generative and discriminative models, pushing for a more unified understanding.
Understanding the optimal balance between generative and discriminative learning is crucial for developing more human-aligned and efficient AI systems, potentially leading to breakthroughs in computer vision and broader AI applications.
The ability to continuously interpolate between discriminative and generative training within a fixed architecture provides a new methodological tool for AI research, allowing for more precise analysis of learning objectives.
- · AI researchers
- · Computer vision developers
- · Robotics
- · Autonomous systems
- · AI models without human alignment
- · Less agile AI research methodologies
Improved human-like visual representations in AI models.
Faster development and deployment of more robust and intuitively understandable AI systems.
Enhanced trust and adoption of AI in sensitive applications requiring human-level perception and reasoning.
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Read at arXiv cs.AI