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..
Tensorized Projection for High-Dimensional Binary Embedding
Authors: Weixiang Hong, Jingjing Meng, Junsong Yuan
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental comparisons with state-of-the-art methods over various visual tasks demonstrate both the efficiency and performance advantages of our proposed TP... Extensive experiments show that our approach not only achieves competitive performance in compact-bit case but also outperforms state-of-the-art methods in long-bit scenario. To evaluate Tensorized Projection (TP), we conduct experiments on three tasks... |
| Researcher Affiliation | Academia | Weixiang Hong, Jingjing Meng, Junsong Yuan School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore EMAIL |
| Pseudocode | No | The paper describes the optimization steps and the Alternating Least Square (ALS) method with mathematical equations and descriptive text but does not include a clearly labeled pseudocode block or algorithm box. |
| Open Source Code | No | The paper does not provide any statement about open-sourcing the code for its methodology or a link to a code repository. |
| Open Datasets | Yes | We use the pre-trained Alex Net (Krizhevsky, Sutskever, and Hinton 2012) provided by Caffe (Jia et al. 2014) to extract DNN features for one million images in MIRFLICKR-1M dataset (Huiskes and Lew 2008). We refer to this dataset as DNN4096. GIST-960 (Jegou, Douze, and Schmid 2008) dataset, which contains one million 960-dimensional GIST features (Oliva and Torralba 2001) and 10, 000 queries. Holidays + MIRFlickr-1M dataset (Jegou, Douze, and Schmid 2008). CIFAR-10 dataset (Krizhevsky 2009). |
| Dataset Splits | Yes | We use the pre-trained Alex Net (Krizhevsky, Sutskever, and Hinton 2012)... to extract DNN features for one million images in MIRFLICKR-1M dataset (Huiskes and Lew 2008). We randomly pick 1,000 samples as queries. We first fine-tune the pre-trained model provided by Caffe (Jia et al. 2014) on the training set of CIFAR-10, then we use the fine-tuned model to generate features for both training images and testing images. |
| Hardware Specification | Yes | The machine we use is equipped with Intel Xeon CPUs E5-2630 (2.30GHz) and 96 GB memory. We gratefully acknowledge the support of NVAITC (NVIDIA AI Technology Centre) for their donation of a Tesla K80 and M60 GPU used for our research at the ROSE Lab. |
| Software Dependencies | No | All experiments are conducted using Matlab, while the evaluation of encoding time is implemented in C++ using a single thread. The paper does not provide specific version numbers for Matlab, C++, or any other libraries used. |
| Experiment Setup | Yes | For the hyperparameter β in Equation 9, we experimentally find that a fixed β = 1 leads to competitive accuracy, and the accuracy is insensitive to the choice of β (we tried from 0.1 to 100). So we simply fix β = 1 for all experiments in this work. In practice, it takes around 10 iterations to converge as shown in Figure 1. Based on these observations, we fix the TT-rank to 4 to balance the accuracy and efficiency in all following experiments. |