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Evidence record 3855 · automatically gathered

Not Too Generative, Not Too Discriminative: The Human Alignment Sweet Spot

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

Record details

Published: 22 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment · Environment
Retrieved: 14 July 2026

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These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (22 May 2026), “Not Too Generative, Not Too Discriminative: The Human Alignment Sweet Spot,” evidence record 3855, https://ethics.ai/record/3855 (originally published by arXiv).

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