Perceptual independence increases with depth in a generative adversarial network
Bibliographic record
Abstract
According to the efficient coding hypothesis (Barlow, 1959), neural coding serves to reduce statistical dependencies present in visual images, eliminating redundancies and thus yielding more compact perceptual representations. It has been shown (Kersten, 1987) that humans can invert these representations to generate accurate perceptual predictions of information missing in the image. Like the brain, Generative adversarial networks (GANs) learn a hierarchical representation that can be inverted to make predictions in the image domain. Here, we explore to what degree GANs can serve as a model for the progression in statistical independence seen in the brain. We do this by assessing the degree to which observers can estimate missing information at each layer of a Wasserstein GAN (wGAN). If the GAN’s encoding of the image parallels our neural encoding, human ability to predict unit activations should decline from shallow layers near the image domain to deeper layers within the wGAN. Method: A wGAN was trained on CIFAR10. Images were generated by randomly sampling values in the latent layer, and then propagating activations through the network to the image. In each trial, a target unit in one of five layers of the wGAN was randomly selected for analysis. Observer estimates of target activations were identified with a nested adjustment task (Bethge, 2007) involving a telescoping sequence of image triplets. Each triplet was generated using three different values for the target unit, and observers had to select the image that appeared most natural. Results: We observed a systematic decrease in Pearson correlation between true and estimated values of target unit activations as we advanced from shallow layers near the image domain to deep layers, reflecting an increase in perceptual independence as a function of depth. Thus wGANs may form a reasonable model for the progressive elimination of perceptual redundancies in human visual coding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".