Evolving Convolutional Neural Network and Its Application in Fine-Grained Visual Categorization
Why this work is in the frame
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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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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 it