Contextually Affinitive Neighborhood Refinery for Deep Clustering
Authors: Chunlin Yu, Ye Shi, Jingya Wang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments 4.1 Datasets and Settings 4.2 Implementations 4.3 Comparison with the State-of-the-Art 4.4 Ablation Study Table 1: Clustering result comparison (in percentage %) with the state-of-the-art methods on five benchmarks. |
| Researcher Affiliation | Academia | 1 Shanghai Tech University 2 Shanghai Engineering Research Center of Intelligent Vision and Imaging |
| Pseudocode | Yes | Algorithm 1 The proposed algorithm Co NR |
| Open Source Code | Yes | Code is available at: https://github.com/cly234/Deep Clustering-Con NR. |
| Open Datasets | Yes | CIFAR-10 [22], CIFAR-20 [22], STL-10 [7], Image Net-10 [4], Image Net-Dogs [4]. |
| Dataset Splits | No | For the dataset split, both train and test data are used for CIFAR-10 and CIFAR-20, both labeled and unlabeled data are used for STL-10, and only training data of Image Net-10 and Image Net-Dogs are used, which is strictly the same setting with [17, 38, 23, 24]. The paper does not explicitly state a validation dataset split. |
| Hardware Specification | No | The paper does not explicitly state the specific hardware (e.g., CPU, GPU models, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like ResNet and SGD optimizer, but does not provide specific version numbers for any programming languages, libraries, or frameworks (e.g., Python, PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | All datasets are trained with 1000 epochs, where the first 800 epochs are trained with standard BYOL loss LI sim, and the remaining 200 epochs are trained with our proposed LGAF sim . We adopt the stochastic gradient descent (SGD) optimizer and the cosine decay learning rate schedule with 50 epochs of warmup. The base learning rate is 0.05 with a batch size of 256. ... For group-aware concordance, we set k, k1, k2 to 20,30,10 for Image Net-Dogs and 10,10,2 for other datasets. |