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..
CliqueCNN: Deep Unsupervised Exemplar Learning
Authors: Miguel A. Bautista, Artsiom Sanakoyeu, Ekaterina Tikhoncheva, Bjorn Ommer
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experimental evaluation the proposed approach significantly improves over state-of-the-art approaches for posture analysis and retrieval by learning a general feature representation for human pose that can be transferred across datasets. |
| Researcher Affiliation | Academia | Miguel A. Bautista , Artsiom Sanakoyeu , Ekaterina Sutter, Björn Ommer Heidelberg Collaboratory for Image Processing IWR, Heidelberg University, Germany EMAIL |
| Pseudocode | No | The paper describes its algorithm and optimization steps using equations and textual explanations, but it does not provide a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | Project on Git Hub: https://github.com/asanakoy/cliquecnn |
| Open Datasets | Yes | The Olympic Sports dataset [16] is a video compilation of different sports competitions. The Leeds Sports Dataset [12] is the most widely used benchmark for pose estimation. ... PASCAL VOC 2007 dataset. |
| Dataset Splits | No | The paper mentions training data and test data but does not explicitly specify a separate validation set or its split/size for any of the datasets used. |
| Hardware Specification | Yes | We are grateful to the NVIDIA corporation for donating a Titan X GPU. |
| Software Dependencies | No | The paper mentions 'caffe implementation' but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Batches for training the network are obtained by solving the optimization problem in Eq. (1) with B = 100, K = 100, and r = 20 and fine-tuning the model for 105 iterations. |