CliqueCNN: Deep Unsupervised Exemplar Learning
Authors: Miguel A. Bautista, Artsiom Sanakoyeu, Ekaterina Tikhoncheva, Bjorn Ommer
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | 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 firstname.lastname@iwr.uni-heidelberg.de |
| 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. |