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 [1].
Top-Down Deep Clustering with Multi-Generator GANs
Authors: Daniel P. M. de Mello, Renato M. Assunção, Fabricio Murai7770-7778
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct several experiments to evaluate the proposed method against recent DC methods, obtaining competitive results. Last, we perform an exploratory analysis of the hierarchical clustering tree that highlights how accurately it organizes the data in a hierarchy of semantically coherent patterns. |
| Researcher Affiliation | Collaboration | Daniel P. M. de Mello 1, Renato M. Assunc ao 2, 1, Fabricio Murai 1 1 Universidade Federal de Minas Gerais, 2 Esri Inc. |
| Pseudocode | Yes | Algorithm 1: Split... Algorithm 2: Raw Split... Algorithm 3: Refinement... Algorithm 4: Train Refin Group... |
| Open Source Code | Yes | Code available at github.com/dmdmello/HC-MGAN and supplementary/implementation details at arxiv.org/abs/2112.03398. |
| Open Datasets | Yes | We consider three datasets: MNIST (Le Cun et al. 1998), Fashion MNIST (FMNIST) (Xiao, Rasul, and Vollgraf 2017) and Stanford Online Products (SOP) (Oh Song et al. 2016). |
| Dataset Splits | No | The paper states 'we used all available images for each dataset' and discusses metrics computed on the results, implying the entire dataset is used for clustering and evaluation. However, it does not specify explicit train/validation/test splits (e.g., percentages, sample counts, or predefined splits) for model training or evaluation in a traditional supervised learning sense. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory used for running the experiments. It only mentions 'As unsupervised tasks forbid hyperparameter tuning, we used only slightly different tunings for each dataset...' |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies (e.g., Python version, deep learning framework version like PyTorch or TensorFlow, or specific library versions). |
| Experiment Setup | No | The paper mentions 'slightly different tunings for each dataset' but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs, optimizer settings) or other concrete system-level training configurations in the main text. |