AI Art Neural Constellation: Revealing the Collective and Contrastive State of AI-Generated and Human Art
Why this work is in the frame
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Bibliographic record
Abstract
Discovering the creative potentials of a random signal to various artistic expressions in aesthetic and conceptual richness is a ground for the recent success of generative machine learning as a way of art creation. To understand the new artistic medium better, in this work, we comprehensively analyze AI-generated art within the context of human art heritage using our dataset, "ArtConstellation," comprising annotations for 6,000 WikiArt and 3,200 AI-generated artworks. After training various generative models, we compare the produced art samples with WikiArt data using the last hidden layer of a deep-CNN trained for style classification. By interpreting neural representations with important artistic concepts like Wölfflin’s principles, we find that AI-generated artworks align with modern period art concepts (1800 - 2000). Out-Of-Distribution (OOD) and In-Distribution (ID) detection in CLIP space reveal that AI-generated art is ID to human art with landscapes and geometric abstract figures but OOD with deformed and twisted figures, showcasing unique characteristics. A human survey on emotional experience indicates color composition and familiar subjects as key factors in likability and emotions. We introduce our methodologies and dataset, "ArtNeural-Constellation," as a framework for contrasting human and AI-generated art. Code and data are available here.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it