Image Clustering with External Guidance
Authors: Yunfan Li, Peng Hu, Dezhong Peng, Jiancheng Lv, Jianping Fan, Xi Peng
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that TAC achieves state-of-the-art performance on five widely used and three more challenging image clustering benchmarks, including the full Image Net-1K dataset. |
| Researcher Affiliation | Collaboration | 1School of Computer Science, Sichuan University, Chengdu, China 2AI Lab at Lenovo Research, Beijing, China. |
| Pseudocode | No | The paper describes the proposed method in prose and mathematical equations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code can be accessed at https://github.com/ XLearning-SCU/2024-ICML-TAC. |
| Open Datasets | Yes | To evaluate the performance of our TAC, we first apply it to five widely-used image clustering datasets including STL10 (Coates et al., 2011), CIFAR-10 (Krizhevsky & Hinton, 2009), CIFAR-20 (Krizhevsky & Hinton, 2009), Image Net10 (Chang et al., 2017b), and Image Net-Dogs (Chang et al., 2017b). ... Thus, we further evaluate the proposed TAC on three more complex datasets with larger cluster numbers, including DTD (Cimpoi et al., 2014), UCF-101 (Soomro et al., 2012), and Image Net-1K (Deng et al., 2009). |
| Dataset Splits | No | Following recent deep clustering works (Van et al., 2020; Dang et al., 2021a), we train and evaluate TAC on the train and test splits, respectively. Table 1 also clearly shows only 'Training Split' and 'Test Split' columns for all datasets, without a separate validation split for hyperparameter tuning. |
| Hardware Specification | Yes | All experiments are conducted on a single Nvidia RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions using a pre-trained CLIP model and Adam optimizer, but does not provide specific version numbers for software dependencies like Python, PyTorch, or other libraries. |
| Experiment Setup | Yes | We train f and g by the Adam optimizer with an initial learning rate of 1e 3 for 20 epochs, with a batch size of 512. We fix τ = 5e 3, ˆτ = 0.5, and α = 5.0 in all the experiments. The only exception is that on UCF-101 and Image Net-1K, we change ˆτ to 5.0, batch size to 8192, and training epochs to 100, catering to the large cluster number. |