Towards Debiased Generalized Category Discovery
Bibliographic record
Abstract
Generalized Category Discovery (GCD) aims at classifying unlabeled training data coming from old and novel classes by leveraging the information of partially labeled old classes. In this paper, we reveal that existing methods often suffer from competition between new and old classes, where the focus on learning new classes often results in a notable performance degradation on the old classes. Moreover, we delve into the reason behind this problem: the GCD classifier can be overconfident and biased towards the new class. With this insight, we propose Debiased GCD (DeGCD), a simple but effective approach that mitigates the bias caused by the overconfidence from new categories by a debiased head. Specifically, we first propose semantic calibration loss that aids the GCD classifier in debiasing by enforcing neighborhood prediction consistency with the latent representation of the debiased head. Furthermore, a debiased contrastive objective is proposed to refine the similarity matrix from the GCD classifier and the debiased classifier, suppressing the overconfidence in new classes in unlabeled data. In addition, an alignment constraint loss is designed to prevent damaging the distribution of the old categories caused by overconfidence in the new categories. Experiments on various datasets shows DeGCD achieves state-of-the-art performance and maintains a good balance between new and old classes. In addition, this method can be seamlessly adapted to other GCD methods, not only to achieve further performance gains but also to effectively balance the performance of the new class with that of the old class.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".