Self-Conditioned Generative Data Augmentation for Image Classification
Bibliographic record
Abstract
Data augmentation is a widely used technique that not only increases the size of the training dataset but also improves the generalization ability of deep learning models. Traditional image augmentation methods primarily rely on geometric transformations. More recently, approaches utilizing pre-trained generative models have been proposed to further enhance dataset diversity by synthesizing new data. However, these approaches often suffer from several limitations, including the generation of distorted objects, repetitive or unrealistic background patterns, and a dependency on carefully crafted prompts to produce semantically meaningful results. In this paper, we propose Self-Conditioned Generative Data Augmentation (SCGDA), a novel pipeline that generates images under diverse conditions while preserving the visual cues of the target objects without requiring model tuning or manual prompt engineering. SCGDA effectively generates image through three automated generative steps, reflecting diverse conditions while preserving the essential visual cues of the target object. We applied SCGDA to the ResNet models and evaluated it on the reduced ImageNet datasets. Experimental results show that our proposed method improves classification performance by up to 8.8% on the ResNet model, demonstrating its effectiveness.
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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 | 0.000 | 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".