Details (Don't) Matter: Isolating Cluster Information in Deep Embedded Spaces
Authors: Lukas Miklautz, Lena G. M. Bauer, Dominik Mautz, Sebastian Tschiatschek, Christian Böhm, Claudia Plant
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate all algorithms with six different data sets focusing on common DC benchmarks like MNIST [Le Cun et al., 1998], Fashion-MNIST [Xiao et al., 2017] and USPS. Additionally, we use a data set based on real world images of traffic signs (GTSRB) [Houben et al., 2013], recorded under different camera angles and daylight conditions, and two synthetic data sets to show the impact of irrelevant information on DC performance. |
| Researcher Affiliation | Academia | 1Faculty of Computer Science, University of Vienna, Vienna, Austria 2Ludwig-Maximilians-Universit at M unchen, Munich, Germany 3ds:Uni Vie, Austria, 4MCML, Germany |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | We uploaded our code and supplement at https://gitlab.cs.univie.ac.at/lukas/acedec public. |
| Open Datasets | Yes | We evaluate all algorithms with six different data sets focusing on common DC benchmarks like MNIST [Le Cun et al., 1998], Fashion-MNIST [Xiao et al., 2017] and USPS. Additionally, we use a data set based on real world images of traffic signs (GTSRB) [Houben et al., 2013], recorded under different camera angles and daylight conditions, and two synthetic data sets to show the impact of irrelevant information on DC performance. |
| Dataset Splits | No | The paper refers to "MNIST-Full" and "MNIST-Test" in Table 1, implying standard splits, but does not explicitly provide specific details on the train/validation/test dataset splits (percentages, sample counts, or methodology) within the main body of the paper. It refers to the supplementary material for further details: "Further explanations about the experiments, data sets, information on our hardware setup, compared methods, and all additional experiments are in the SP (Sec. 2)." |
| Hardware Specification | No | Further explanations about the experiments, data sets, information on our hardware setup, compared methods, and all additional experiments are in the SP (Sec. 2). |
| Software Dependencies | No | The paper refers to supplementary material for 'training budget, learning rate, optimizer, etc., see the SP (Sec. 2.4) for details)' but does not explicitly list specific software dependencies with version numbers in the main text. |
| Experiment Setup | No | We used the same settings for the training of all DC methods (training budget, learning rate, optimizer, etc., see the SP (Sec. 2.4) for details). |