Balanced Decoupled Spatial Convolution for CNNs
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we are interested in designing lightweight CNNs by decoupling the convolution along the spatial and channel dimension. Most existing decoupling techniques focus on approximating the filter matrix through decomposition. In contrast, we provide a decoupled view of the standard convolution to separate the spatial information and the channel information. The resulting decoupled process is exactly equivalent to the standard convolution. Inspired from our decoupled view, we propose an effective structure, balanced decoupled spatial convolution (BDSC), to relax the sparsity of the filter in spatial aggregation by learning a spatial configuration and reduce the redundancy by reducing the number of intermediate channels. We also designed an adaptive spatial configuration, which is simply adding a nonlinear activation layer [rectified linear units (ReLU)] after the intermediate output. Our experiments verify that the adaptive spatial configuration can improve the classification performance without extra cost. In addition, our BDSC achieves comparable classification performance with the standard convolution but with a smaller model size on Canadian Institute for Advanced Research (CIFAR)-100, CIFAR-10, and ImageNet. To show the potential of further reducing the redundancy of across channel-domain convolution, we also show experiments of our models with a designed lightweight across channel-domain convolution. Finally, we show in our experiments that our models achieve superior performance than the state-of-the-art models.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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