Deep Latent Low-Rank Fusion Network for Progressive Subspace Discovery
Authors: Zhao Zhang, Jiahuan Ren, Zheng Zhang, Guangcan Liu
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | 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. |