Learning Receptive Field Size by Learning Filter Size
Bibliographic record
Abstract
Covering various receptive fields within a layer is essential to effectively recognize the objects of various sizes and types at a specific layer for a convolutional neural network (CNN). In this work, we propose a novel adaptive learning method which learns the filter size (i.e. the kernel size of a convolutional filter) and distribution to learn the receptive field size. Directly optimizing with respect to the filter size is challenging because the filter size is discrete. To overcome this, we propose a masking technique, which enables the automatic allocation of resources over filters of different sizes and leads to efficient optimization. Through our proposed trainable formulation of the mask, the network self-organizes its structure through the standard backpropagation. The proposed adaptive CNN can be generalized to any single-path structures and multi-path structures as well. The effectiveness of our proposed approach is validated by several benchmark datasets compared with various previous structures on the image classification task for diverse network depths and widths. Furthermore, we demonstrate our adaptive CNN trained on a large-scale dataset can yield improved performance when applying to a transfer learning.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".