Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search
Bibliographic record
Abstract
Meta-learning techniques based on neural architecture search (NAS) show excellent performance in the design of learning models used in deep neural networks. In particular, when NAS is applied to design a convolutional neural network (CNN) for image recognition, the performance of the network when evaluating public benchmark datasets such as CIFAR10 and ImageNet exceeds that of hand-designed models. Nevertheless, there are very few cases wherein NAS has been applied to real-world problems, i.e. recognition problems with a limited dataset. We proposed a method in which the NAS technique does not require a proxy task for the scene text recognition (STR) framework to apply the NAS method to a new image recognition field. Therefore, we proposed an architecture space for CNN-based modules in the STR framework and applied the ProxylessNAS method, enabling end-to-end training while meta learners design a new model that requires only a single commonly used GPU (approximately 100 GPU hours). To evaluate the STR model obtained by the proposed NAS method, seven STR benchmark datasets were used. Finally, the obtained model could achieve a performance similar to that of the ideal model in terms of accuracy and number of parameters. We thus confirm that the model design based on NAS can be effectively applied to STR scenarios.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".