Evolving Convolutional Neural Network and Its Application in Fine-Grained Visual Categorization
Bibliographic record
Abstract
Fine-grained visual categorization is one of the challenges in computer vision due to the high intra-class but low inter-class variances. Convolutional neural networks (CNNs) are widely used to solve this problem. However, a huge number of clearly labeled images are usually required to train a CNN model for a high precision, which may be quite costly and time consuming. To overcome this problem, in this paper, a novel evolving CNN (ECNN) is proposed, which can efficiently utilize the limited clearly labeled images and a large number of weakly labeled images. The overall framework contains two parts: one for collecting the weakly labeled images from the Internet by Web crawlers; and the other for updating the CNN classifier. Specifically, several different search engines are adopted to collect the weakly labeled images, in order to get relatively comprehensive results. The proposed method is demonstrated on several datasets, including CIFAR-10, Oxford pets, and Chinese food dataset. The results show that ECNN outperforms the traditional CNN and achieves the state-of-the-art in most cases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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 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".