Towards Distribution-Agnostic Generalized Category Discovery
Authors: Jianhong Bai, Zuozhu Liu, Hualiang Wang, Ruizhe Chen, Lianrui Mu, Xiaomeng Li, Joey Tianyi Zhou, YANG FENG, Jian Wu, Haoji Hu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare Ba Con with state-of-the-art methods from two closely related fields: imbalanced semi-supervised learning and generalized category discovery. The effectiveness of Ba Con is demonstrated with superior performance over all baselines and comprehensive analysis across various datasets. |
| Researcher Affiliation | Collaboration | 1Zhejiang University, 2The Hong Kong University of Science and Technology, 3Centre for Frontier AI Research, A*STAR, 4Angelalign Technology Inc. |
| Pseudocode | Yes | The pseudo-code of Ba Con can be found in Appendix B. Algorithm 1 The overall pipeline of Ba Con. Algorithm 2 The test stage strategy of Ba Con. |
| Open Source Code | Yes | Our code is available at: https://github.com/Jianhong Bai/Ba Con. |
| Open Datasets | Yes | We conduct experiments on four popular datasets. CIFAR-10-LT/CIFAR-100-LT are long-tail subsets sampled from the original CIFAR10/CIFAR100 [10]. Image Net-100-LT is proposed by [27] with 12K images sampled from Image Net-100 [57]... Places-LT [40] contains about 62.5K images sampled from the large-scale scene-centric Places dataset [80]... |
| Dataset Splits | No | The paper describes training and testing sets, but does not explicitly mention a separate validation dataset split with proportions or counts for hyperparameter tuning or model selection. |
| Hardware Specification | Yes | We implement all our techniques using Py Torch [47] and conduct the experiments using a RTX3090 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch [47]' but does not specify its version or any other software dependencies with their respective version numbers. |
| Experiment Setup | Yes | We train with a batch size of 256, and an initial learning rate of 0.1 decayed with a cosine schedule. We train for 200 epochs on each dataset. The temperature τ is set to 1.0, the hyper-parameter p and k is set to 0.5, the sampling rates α = 0.8, β = 0.5, and the re-estimate interval r is 10 (epochs). |