Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
Authors: Zhuxi Jiang, Yin Zheng, Huachun Tan, Bangsheng Tang, Hanning Zhou
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Quantitative comparisons with strong baselines are included in this paper, and experimental results show that Va DE significantly outperforms the state-of-the-art clustering methods on 5 benchmarks from various modalities. |
| Researcher Affiliation | Collaboration | 1Beijing Institute of Technology, Beijing, China 2Tencent AI Lab, Shenzhen, China 3Hulu LLC., Beijing, China |
| Pseudocode | No | The paper describes the generative process and optimization steps in narrative text and mathematical equations, but it does not include formal pseudocode blocks or algorithms. |
| Open Source Code | Yes | The code of Va DE is available at https://github.com/slim1017/Va DE. |
| Open Datasets | Yes | MNIST [Le Cun et al., 1998], HHAR [Stisen et al., 2015], REUTERS-10K [Lewis et al., 2004], REUTERS [Lewis et al., 2004] and STL-10 [Coates et al., 2011]. |
| Dataset Splits | No | The paper discusses training parameters like learning rates and pretraining but does not explicitly specify dataset splits for training, validation, and testing. It mentions a test set later but no clear validation split details. |
| Hardware Specification | No | The paper describes the network architectures and training parameters but does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used for the experiments. |
| Software Dependencies | No | The paper mentions the use of the Adam optimizer and techniques like t-SNE and ResNet-50, but it does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions or library versions). |
| Experiment Setup | Yes | Specifically, the architectures of f and g in Equation 1 and Equation 10 are 10-2000-500-500-D and D-500-500-2000-10, respectively, where D is the input dimensionality. All layers are fully connected. Adam optimizer [Kingma and Ba, 2015] is used to maximize the ELBO of Equation 9, and the mini-batch size is 100. The learning rate for MNIST, HHAR, Reuters-10K and STL-10 is 0.002 and decreases every 10 epochs with a decay rate of 0.9, and the learning rate for Reuters is 0.0005 with a decay rate of 0.5 for every epoch. |