ADNet: Adaptively Dense Convolutional Neural Networks
Bibliographic record
Abstract
Convolutional neural networks (CNNs) have demonstrated great success in vision tasks. However, most existing architectures still suffer from low feature reuse efficiency. In this paper, we present a layer attention based Adaptively Dense Network (ADNet) by adaptively determining the reuse status of hierarchical preceding features. Specifically, a dense residual aggregation strategy is developed to fuse multi-level internal representations in an effective manner. Furthermore, a novel layer attention mechanism is proposed to explicitly model the interrelationship among layers to automatically adjust the density of the network. It is worth noting that existing ResNets and DenseNets are both special cases of our ADNet. Extensive experiments demonstrate that the proposed architecture consistently and indubitably achieves competitive results in accuracy on benchmark datasets (CIFAR10, CIFAR100, and SVHN), while at the same time remarkably reduces computational costs and memory space. Visualization and analysis on layer-wise attention further provide better understanding on the density of feature reuse in Deep Networks.
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.
How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".