DE-CapsNet: A Diverse Enhanced Capsule Network with Disperse Dynamic Routing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Capsule Network (CapsNet) is a methodology with good prospects in visual tasks, since it can keep a stronger relationship of spatial information than Convolutional Neural Networks (CNNs). However, the current Capsule Network do not provide performance as expected on several benchmark data sets with complex data and backgrounds. Inspired by the multiple capsules of Diverse Capsule Network (DCNet++) and the Spatial Group-wise Enhance (SGE) mechanism, we propose the Diverse Enhanced Capsule Network (DE-CapsNet), a hierarchical architecture which uses residual convolutional layers and the position-wise dot product to build diverse enhanced primary capsules with various scales of images for complex data. The architecture adopts the Sigmoid function in a dynamic routing algorithm to get a more uniform distribution of routing coefficients which obviously distinguishes the assignment probabilities between capsules. DE-CapsNet achieved state-of-the-art accuracy on Canadian Institute For Advanced Research (CIFAR-10) in the Capsule Network field and provided better performance than the ensemble of seven CapsNets on Fashion-Modified National Institue of Standards and Technology database (F-MNIST) while achieving a 50.3% reduction in the number of parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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