Distant Pedestrian Detection in the Wild using Single Shot Detector with\n Deep Convolutional Generative Adversarial Networks
Bibliographic record
Abstract
In this work, we examine the feasibility of applying Deep Convolutional\nGenerative Adversarial Networks (DCGANs) with Single Shot Detector (SSD) as\ndata-processing technique to handle with the challenge of pedestrian detection\nin the wild. Specifically, we attempted to use in-fill completion (where a\nportion of the image is masked) to generate random transformations of images\nwith portions missing to expand existing labelled datasets. In our work, GAN\nhas been trained intensively on low resolution images, in order to neutralize\nthe challenges of the pedestrian detection in the wild, and considered humans,\nand few other classes for detection in smart cities. The object detector\nexperiment performed by training GAN model along with SSD provided a\nsubstantial improvement in the results. This approach presents a very\ninteresting overview in the current state of art on GAN networks for object\ndetection. We used Canadian Institute for Advanced Research (CIFAR), Caltech,\nKITTI data set for training and testing the network under different resolutions\nand the experimental results with comparison been showedbetween DCGAN cascaded\nwith SSD and SSD itself.\n
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".