Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Domain-Adaptive Self-Supervised Face & Body Detection in Drawings
Authors: Barış Batuhan Topal, Deniz Yuret, Tevfik Metin Sezgin
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our combined architecture yields detectors with state-of-the-art (SOTA) and near-SOTA performance using minimal annotation effort. Our code can be accessed from https://github. com/barisbatuhan/DASS_Detector. |
| Researcher Affiliation | Academia | Barıs Batuhan Topal1 , Deniz Yuret1 , Tevfik Metin Sezgin1 1Department of Computer Engineering, KUIS AI Center, Koc University EMAIL |
| Pseudocode | No | The paper includes figures illustrating the model architecture and training process, but it does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code can be accessed from https://github. com/barisbatuhan/DASS_Detector. |
| Open Datasets | Yes | We process COCO and WIDER FACE datasets with 11 different styles. [...] We also utilized 198,657 pages from COMICS and leveraged i Cartoon Face, Manga 109 pages, Comic2k, Watercolor2k, and Clipart1k images. [...] COCO [Lin et al., 2014], WIDER FACE [Yang et al., 2016]. [...] COMICS [Iyyer et al., 2016]. [...] i Cartoon Face [Zheng et al., 2020]. [...] Manga 109 [Matsui et al., 2017]. [...] DCM 772 [Nguyen et al., 2018]. [...] Comic2k, Watercolor2k, and Clipart1k [Inoue et al., 2018]. |
| Dataset Splits | Yes | i Cartoon Face contains a significant amount of face data with its 50,000 training and 10,000 validation images. |
| Hardware Specification | Yes | In all variations and experiments, the batch size is set to 16, and one Tesla T4 GPU is used. |
| Software Dependencies | No | The paper does not explicitly provide specific version numbers for software dependencies such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | In all variations and experiments, the batch size is set to 16, and one Tesla T4 GPU is used. [...] At stages 1 and 3, the learning rate is fixed at 0.001. The highest-scoring checkpoints in the evaluation set among 350 epochs are chosen as the final models. [...] For the teacher-student network, the learning rate is set as 0.0001, and the best checkpoints in 10000 iterations are taken as final models. [...] In our final model, we set Φ to 500, β to 2, d to 0.9996, and positive and negative thresholds (ctthres pos and ctthres neg ) to 0.5. |