On The Relationship Between Continual Learning and Long-Tailed Recognition
Bibliographic record
Abstract
Real-world datasets often exhibit long-tailed distributions, where a few dominant "Head" classes have abundant samples while most "Tail" classes are severely underrepresented, leading to biased learning and poor generalization for the Tail. We present a theoretical framework that reveals a previously undescribed connection between Long-Tailed Recognition (LTR) and Continual Learning (CL), the process of learning sequential tasks without forgetting prior knowledge. Our analysis demonstrates that, for models trained on imbalanced datasets, the weights converge to a bounded neighborhood of those trained exclusively on the Head, with the bound scaling as the inverse square root of the imbalance factor. Leveraging this insight, we introduce Continual Learning for Long-Tailed Recognition (CLTR), a principled approach that employs standard off-the-shelf CL methods to address LTR problems by sequentially learning Head and Tail classes without forgetting the Head. Our theoretical analysis further suggests that CLTR mitigates gradient saturation and improves Tail learning while maintaining strong Head performance. Extensive experiments on CIFAR100-LT, CIFAR10-LT, ImageNet-LT, and Caltech256 validate our theoretical predictions, achieving strong results across various LTR benchmarks. Our work bridges the gap between LTR and CL, providing a principled way to tackle imbalanced data challenges with standard existing CL strategies.
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".