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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".