Visualizing Feature Maps for Model Selection in Convolutional Neural Networks
Bibliographic record
Abstract
Convolutional neural networks (CNN) are increasingly being used to achieve state-of-the-art performance for various plant phenotyping and agricultural tasks. While con-structing such CNN models, a common problem is over-parameterization, which may lead to a model becoming overfit on a training dataset. This problem is particularly relevant for plant datasets with limited variation and/or small samples sizes. Inspection of the loss and accuracy curves is a common way to detect overfitting in a CNN model, but it provides little insight into how the model could be improved. There are several reasons contributing to the overfitting of a CNN model; however, in this paper, we aim at explaining overfitting in a CNN classification model by analyzing the features learned at various depths of the model. We use three plant phenotyping datasets in our experimental studies. Our comparative analysis between the visualizations of the feature maps obtained from overfit and balanced models reveals that the image background often influences an overfit model’s behavior. Researchers with limited deep learning domain knowledge often attempt to build deeper layer models with the hope of improving per-formance. Using Guided Backpropagation, we show how the pairwise similarity matrix between the visualization of the features learned at different depths can be leveraged to pave a new way to potentially select a better CNN model by removing redundant layers.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".