An approach for automating the design of convolutional neural networks
Bibliographic record
Abstract
Image recognition is an independent field of the computer science nowadays. Image classification is one of its main domains, in which investigated objects can be represented by an image or a video stream. The objective of the image classification is correct assigning of objects to corresponding classes, and there exist many effective approaches for solving this problem. One of the most popular approaches is artificial neural networks, which are a method from the field of machine learning. Despite the fact that neural networks cover a wide range of machine learning problems, they are also able to solve the problem of the image classification. However, there is one more specific approach for neural networks-based images classification that applies the deep learning conception. The best-known deep learning algorithm is called the convolutional neural network (CNN). The CNN uses a principle of using the same parts of a neural network to manipulate with different local parts of an input image. As well as the standard neural network architecture, the convolutional neural network should be fine-tuned for solving a certain problem. Because of the CNN's depth and complexity, the tuning process usually is complex and needs huge computational efforts. In this study, we have proposed an approach for creating ensembles of previously trained convolutional neural networks. The approach allows to increase the performance of the image classification. The results of experiments for image classification problems are presented and discussed. The experiments show that the proposed approach is able to outperform the standard perceptron and single convolutional neural network.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".