Perceptual Evaluations: Evals for Aesthetics — Diego Rodriguez, Krea.ai
KREA.ai's cofounder Diego Rodriguez discusses the critical failure of current AI evaluation metrics in understanding human perception and aesthetics, advocating for a new paradigm of personalized, perceptually-aware evals.
Diego Rodriguez, co-founder of KREA.ai, critiques the current state of AI evaluations, particularly in the realm of generative media, where human perception, opinion, and aesthetics are paramount. The core issue is that even state-of-the-art models, when faced with a simple perceptual task like judging a distorted AI-generated image of a hand, fail to provide a natural, human-like response. While humans react instinctively, advanced AI models perform complex, slow, and ultimately unhelpful analysis, revealing a fundamental disconnect. This is paradoxical, given that these models are trained extensively on human-generated data and preference feedback.
The “Contagion” of Human Limitations
The talk draws a parallel to the history of information theory and media compression. Claude Shannon’s foundational work on communication theory led to technologies like JPEG, MP3, and MP4. These formats are effective because they exploit the inherent limitations of human perception—for instance, our lower sensitivity to color information compared to brightness. By selectively discarding data that humans are unlikely to perceive, these algorithms achieve high compression rates.
This raises a critical question: if our AI models are trained on a vast corpus of internet data, which is saturated with compressed media, are we inadvertently training them on our own perceptual flaws? This creates a “contagion” where the limitations of our senses are passed on to the AI, potentially capping its capabilities.
The Failure of Standard Metrics
This problem is exemplified by standard industry metrics like the Fréchet Inception Distance (FID) score. An image with minor JPEG artifacts, which is perceptually identical to the original for a human observer, will receive a drastically worse FID score. This demonstrates that we are measuring what is easy to measure—pixel-level adherence, object counts—rather than what truly matters for user experience and artistic expression.
These metrics fail completely when confronted with subjective or abstract concepts like art. A surrealist painting might be flagged as a “bad” generation because “that’s not how a clock looks,” completely missing the artistic intent and meaning that a human can grasp. The current evaluation paradigm is too rigid and objective for a world of subjective creativity.
Rethinking Evals: Towards a Perceptual Paradigm
To move forward, we must fundamentally rethink our approach. Rodriguez shares an analogy: predicting the car in an age of horses was easy, but predicting the systemic consequence of “traffic” was hard. As engineers and researchers, we must ask, “what are the traffics that we’re missing now?” by focusing on narrow, easily quantifiable problems.
The solution lies in creating evaluation systems that are themselves evaluated—we must “eval our evals.” This new paradigm should be:
- Perceptually Aware: Metrics must account for the nature of the training data (e.g., compression artifacts) and the mechanisms of human perception.
- Subjective and Personalized: Evaluations should incorporate individual human opinion. The rise of powerful translation models, breaking down communication barriers like in the Tower of Babel myth, shows we can now better articulate and process nuanced, subjective feedback.
Machine learning is perfectly suited for this challenge. The problem of determining if an image is “good” is a classic “you will know when you see it” scenario. We can train classifiers on user opinions to develop models that understand aesthetics and personal taste, moving beyond rigid, objective scores. This is the direction KREA.ai is pursuing: building systems for hyper-personalization and aesthetic research to truly empower creative expression.