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 Unsupervised Image Hashing by Maximizing Bit Entropy
Authors: Yunqiang Li, Jan van Gemert2002-2010
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the image datasets Flickr25k, Nus-wide, Cifar-10, Mscoco, Mnist and the video datasets Ucf-101 and Hmdb-51 show that our approach leads to compact codes and compares favorably to the current stateof-the-art. |
| Researcher Affiliation | Academia | Yunqiang Li and Jan van Gemert Computer Vision Lab, Delft University of Technology, Netherlands EMAIL |
| Pseudocode | No | The paper describes the forward and backward passes using mathematical equations and textual explanations, but does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | Yes | and make our code available1. 1https://github.com/liyunqianggyn/Deep-Unsupervised-Image-Hashing |
| Open Datasets | Yes | Flickr25k (Huiskes and Lew 2008) contains 25k images categorized into 24 classes. ... Nus-wide (Chua et al. 2009) has around 270k images ... Cifar-10 (Krizhevsky and Hinton 2009) consists of 60k color images ... Mscoco (Lin et al. 2014b) is a dataset ... Mnist (Le Cun et al. 1998) contains 70k gray-scale ... Ucf-101 (Soomro, Zamir, and Shah 2012) contains 13,320 action instances ... Hmdb-51 (Kuehne et al. 2011) includes 6,766 videos ... |
| Dataset Splits | No | The paper mentions 'validation loss saturates' and 'The hyper-parameters γ is tuned by cross-validation on training set', implying the use of validation. However, explicit details about the proportion or methodology of a dedicated validation dataset split, similar to the training and test splits, are not consistently provided for all datasets. |
| Hardware Specification | No | The paper mentions using pre-trained VGG-16 and ResNet backbones, but it does not specify any particular hardware details such as GPU models, CPU types, or memory used for the experiments. |
| Software Dependencies | No | The paper discusses optimizers (SGD) and mentions pre-trained models (VGG-16, ResNet), but it does not provide specific version numbers for any software dependencies like programming languages, deep learning frameworks (e.g., PyTorch, TensorFlow), or libraries. |
| Experiment Setup | Yes | During training, we use Stochastic Gradient Descent(SGD) as the optimizer with a momentum of 0.9 and a weight decay of 5 10 4 and a batch size of 32. In all experiments, the initial learning rate is set as 0.0001 and we divide the learning rate by 10 when the loss stop decreasing. The hyper-parameters γ is tuned by cross-validation on training set and set as γ = 3 1 N K . |