Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation
Authors: Yaling Tao, Kentaro Takagi, Kouta Nakata
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through detailed experiments and examination, we show that the approach can be adapted to learning a latent space for clustering. In evaluations of image clustering using CIFAR-10 and Image Net-10, our method achieves accuracy of 81.5% and 95.4%, respectively. |
| Researcher Affiliation | Industry | Yaling Tao, Kentaro Takagi & Kouta Nakata Corporate R&D Center, Toshiba Corporation 1, Komukai Toshiba-cho, Saiwai-ku, Kawasaki, Kanagawa, Japan {yaling1.tao,kentaro1.takagi,kouta.nakata}@toshiba.co.jp |
| Pseudocode | No | The paper does not include pseudocode or clearly labeled algorithm blocks; the methods are described in text and mathematical formulas. |
| Open Source Code | Yes | Our Py Torch Paszke et al. (2019) implementation of IDFD is available at https://github.com/TTN-YKK/Clustering_ friendly_representation_learning. |
| Open Datasets | Yes | We conducted experiments using five datasets: CIFAR-10 Krizhevsky et al. (2009), CIFAR-100 Krizhevsky et al. (2009), STL-10 Coates et al. (2011), Image Net-10 Deng et al. (2009), and Image Net-Dog Deng et al. (2009). |
| Dataset Splits | No | The paper mentions "Specifically, the training and testing sets of dataset STL-10 were jointly used in our experiments," but does not provide explicit training/validation/test splits with percentages, sample counts, or clear references to predefined splits for all datasets, nor does it specify a separate validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, memory, or other detailed computer specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using "Py Torch" but does not specify a version number or list other software dependencies with their corresponding version numbers. |
| Experiment Setup | Yes | The dimension of latent feature vectors was set to d = 128, and a stochastic gradient descent optimizer with momentum β = 0.9 was used. The learning rate lr was initialized to 0.03, then gradually scaled down after the first 600 epochs using a coefficient of 0.1 every 350 epochs. The total number of epochs was set to 2000, and the batch size was set to B = 128. Orthogonality constraint weights for IDFO were α = 10 for CIFAR-10 and CIFAR-100 and α = 0.5 for the STL-10 and Image Net subsets. The weight for IDFO α was set according to the orders of magnitudes of the two losses LI and LF O. For IDFD, the weight α was simply fixed at 1. In the main experiments, we set the default temperature parameter value τ = 1 for ID(tuned), IDFO, and IDFD, and τ2 = 2 for IDFD. |