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..
Deep Hashing: A Joint Approach for Image Signature Learning
Authors: Yadong Mu, Zhu Liu
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive quantitative evaluations are conducted. On all adopted benchmarks, our proposed algorithm generates new performance records by significant improvement margins. |
| Researcher Affiliation | Collaboration | Yadong Mu,1 Zhu Liu2 1Institute of Computer Science and Technology, Peking University, China 2Multimedia Department, AT&T Labs, U.S.A. Email: EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Deep Hash Algorithm |
| Open Source Code | No | The paper mentions implementing a customized version of the open-source Caffe but does not explicitly state that the custom code for their proposed method is open-source or provide access details. |
| Open Datasets | Yes | Description of Datasets: We conduct quantitative comparisons over four image benchmarks which represent different visual classification tasks. They include MNIST (Lecun et al. 1998) for handwritten digits recognition, CIFAR10 (Krizhevsky 2009) which is a subset of 80 million Tiny Images dataset and consists of images from ten animal or object categories, Kaggle-Face, which is a Kagglehosted facial expression classification dataset to stimulate the research on facial feature representation learning, and SUN397 (Xiao et al. 2010) which is a large scale scene image dataset of 397 categories. |
| Dataset Splits | No | The paper provides Train/Query Set sizes in Table 1 but does not explicitly describe a separate validation split or its size. |
| Hardware Specification | Yes | All the evaluations are conducted on a large-scale private cluster, equipped with 12 NVIDIA Tesla K20 GPUs and 8 K40 GPUs. |
| Software Dependencies | No | The paper mentions using "open-source Caffe (Jia 2013)" but does not specify its version number or any other software dependencies with their respective versions. |
| Experiment Setup | Yes | In all cases, the learning rate in gradient descent drops at a con-stant factor (0.1 in all of our experiments) until the training converges. |