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 Latent Low-Rank Fusion Network for Progressive Subspace Discovery
Authors: Zhao Zhang, Jiahuan Ren, Zheng Zhang, Guangcan Liu
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive results show that our network can deliver enhanced performance over other related frameworks. ... 6 Experimental Results and Analysis |
| Researcher Affiliation | Academia | Zhao Zhang1,2, Jiahuan Ren2, Zheng Zhang3 and Guangcan Liu4 1 Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology, China 2 School of Computer Science and Technology, Soochow University, China 3 Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen, China 4 School of Information and Control, Nanjing University of Information Science and Technology, China |
| Pseudocode | Yes | Algorithm 1 Solving Eq.(9) by Inexact ALM (l-th layer) |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | Three real image databases are involved, including two face datasets (i.e., CMU PIE [Sim et al., 2003], UMIST [Graham et al., 1998]) and the Fashion MNIST database [Xiao et al., 2017]. The details of used databases are described in Table 1. |
| Dataset Splits | No | The paper describes evaluation procedures for clustering on existing datasets but does not specify distinct training, validation, and test splits for the DLRF-Net model itself in a way that allows reproduction of data partitioning for model training. It mentions 'For each number K of clusters, we choose K categories randomly and the results are averaged over 30 initializations.' |
| Hardware Specification | Yes | We perform all experiments on a PC with Intel (R) Core (TM) i7-7700 CPU @ 3.6 GHz 8G. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | DLRF-Net has one parameter , so we can select the most important one by a linear search from 10^-8, 10^-6, ..., 10^6, 10^8. ... To evaluate the robustness properties, random Gaussian noise with variance 500 is included into the image data. ... For each setting, we average the result over 30 random initialization for NCut. |