Towards Human-Like Invariance: Self-Supervised Learning with Feature-Level Rotation Alignment
Bibliographic record
Abstract
Self-supervised learning (SSL) has made significant progress through joint-embedding methods that learn in-variant representations across transformed views. However, achieving robustness to image rotations remains challenging, as naively incorporating rotation augmentations often degrades performance. Inspired by cognitive studies on human mental rotation, we propose FRTAlign, an SSL framework with feature-level alignment that explicitly mitigates rotation-induced shifts in the representation space. FRTAlign introduces a unified module that learns rotation-equivariant feature transformations and combines them with a lightweight rotation predictor to produce human-inspired rotation-invariant representations. This design enables the model to preserve performance on non-rotated samples while significantly improving robustness to rotated inputs. Through extensive experiments on STL10, Ima-geNet100, and EMNIST, we demonstrate that FRTAlign consistently outperforms baselines in both standard and rotated settings. Further analysis reveals that our method mitigates distributional shifts caused by rotation and is robust to architectural and hyperparameter variations.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".