Deep network with double reuses and convolutional shortcuts
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
Abstract The authors design a novel convolutional network architecture, that is, deep network with double reuses and convolutional shortcuts, in which new compressed reuse units are presented. Compressed reuse units combine the reused features from the first 3 × 3 convolutional layer and the features from the last 3 × 3 convolutional layer to produce new feature maps in the current compressed reuse unit, simultaneously reuse the feature maps from all previous compressed reuse units to generate a shortcut by an 1 × 1 convolution, and then concatenate these new maps and this shortcut as the input to next compressed reuse unit. Deep network with double reuses and convolutional shortcuts uses the feature reuse concatenation from all compressed reuse units as the final features for classification. In deep network with double reuses and convolutional shortcuts, the inner‐ and outer‐unit feature reuses and the convolutional shortcut compressed from the previous outer‐unit feature reuses can alleviate the vanishing‐gradient problem by strengthening the forward feature propagation inside and outside the units, improve the effectiveness of features and reduce calculation cost. Experimental results on CIFAR‐10, CIFAR‐100, ImageNet ILSVRC 2012, Pascal VOC2007 and MS COCO benchmark databases demonstrate the effectiveness of authors’ architecture for object recognition and detection, as compared with the state‐of‐the‐art.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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