Using Dynamic Pruning Technique for Efficient Depth Estimation for Autonomous Vehicles
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
Even with the significant progress that has been achieved in monocular depth estimation in recent years, the need for better real-time inference and reduction in computing resources usage associated with the network performance is persistent. In this paper, an enquiry into the efficacy of pruning on depth estimation models is performed. Encoder-decoder model based on the ResNet-50 backbone architecture employing pruning based on channel prioritization is designed to achieve higher performance and prediction speed. This is while attempting to keep a balance in the trade-off between accuracy and performance of the network. The presented approach is trained and evaluated for outdoor scenery on the KITTI dataset to demonstrate the effectiveness and the performance improvement of the presented framework when compared to similar methods. This shows competitive accuracy when compared to state-of-the-art methods and highlights how pruning can speed up inference time by more than 16% and leading to fewer operations compared to the non-pruned model.
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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 | 0.000 | 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