An Investigation of Cross-dataset Model Generalization of Convolutional Neural Network
Bibliographic record
Abstract
Transfer learning has become increasingly important as a method to leverage pre-trained models on new tasks, potentially saving significant training time and computational resources. Understanding how well these models generalize to new datasets is critical when people are going to use them to solve real-world problems. This research investigates the transfer learning performance of pre-trained models. Specifically, this work evaluates whether these models trained on dataset Canadian Institute for Advanced Research (CIFAR)-10 can still perform well when transferred to another dataset Self-Taught Learning (STL)-10. Both datasets share the same classes, ensuring a meaningful comparison. The models were trained for five epochs on CIFAR-10 and subsequently evaluated on STL-10. Residual Neural Network (ResNet)18 achieved a maximum accuracy of 41.54% on STL-10, while Visual Geometry Group (VGG)16 reached up to 53.36%. These results show the moderate generalization capabilities of the models and suggest that even though transfer learning is not completely ineffective, there are challenges in achieving high performance on new datasets without further fine-tuning. This study aids in comprehending model generalization and provides insight into the potential and limitations of transfer learning in real-world applications.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".