Deep neural networks trained for estimating albedo and illumination achieve lightness constancy differently than human observers
Bibliographic record
Abstract
Abstract Lightness constancy, the ability to create perceptual representations that are strongly correlated with surface albedo despite variations in lighting and context, is a challenging computational problem. Indeed, it has proven difficult to develop image-computable models of how human vision achieves a substantial degree of lightness constancy in complex scenes. Recently, convolutional neural networks (CNNs) have been developed that are proficient at estimating albedo, but little is known about how they achieve this, or whether they are good models of human vision. We examined this question by training a CNN to estimate albedo and illumination in a computer-rendered virtual world, and evaluating both the CNN and human observers in a lightness matching task. In several conditions, we eliminated cues potentially supporting lightness constancy: local contrast, shading, shadows, and all contextual cues. We found that the network achieved a high degree of lightness constancy, outperforming three classic models, and substantially outperforming human observers as well. However, we also found that eliminating cues affected the CNN and humans very differently. Humans had much worse constancy when local contrast cues were made uninformative, but were minimally affected by elimination of shading or shadows. The CNN was unaffected by local contrast, but relied on shading and shadows. These results suggest that the CNN followed an effective strategy of integrating global image cues, whereas humans used a more local strategy. In a follow-up experiment, we found that the CNN could learn to exploit noise artifacts that were correlated with illuminance in ray-traced scenes, whereas humans did not. We conclude that CNNs can learn an effective, global strategy of estimating lightness, which is closer to an optimal strategy for the ensemble of scenes we studied than the computation used by human vision.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".