A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning
Authors: Zitai Wang, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, the empirical results on benchmark datasets not only validate the theoretical results but also demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Collaboration | 1 SKLOIS, Institute of Information Engineering, CAS 2 School of Cyber Security, University of Chinese Academy of Sciences 3 Key Lab. of Intelligent Information Processing, Institute of Computing Tech., CAS 4 School of Computer Science and Tech., University of Chinese Academy of Sciences 5 Alibaba Group 6 School of Cyber Science and Tech., Shenzhen Campus of Sun Yat-sen University 7 BDKM, University of Chinese Academy of Sciences |
| Pseudocode | Yes | Algorithm 1: Principled Learning Algorithm induced by the Theoretical Insights |
| Open Source Code | No | The paper does not provide a link to its source code or explicitly state its release. |
| Open Datasets | Yes | Datasets. We conduct the experiments on four popular benchmark datasets for imbalanced learning. (a) CIFAR-10 and CIFAR-100: Following the protocol in [26, 11, 12]... (b) Image Net-LT and i Naturalist: We use the long-tailed version of the Image Net dataset2 [2] proposed by [27], and i Naturalist3 [5] is a real-world long-tailed dataset. |
| Dataset Splits | Yes | Datasets. We conduct the experiments on four popular benchmark datasets for imbalanced learning. (a) CIFAR-10 and CIFAR-100: Following the protocol in [26, 11, 12]... (b) Image Net-LT and i Naturalist: We use the long-tailed version of the Image Net dataset2 [2] proposed by [27], and i Naturalist3 [5] is a real-world long-tailed dataset. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Pytorch' [51], 'numpy' [52], 'scikit-learn' [53], and 'Torchvision' [54] but does not provide specific version numbers for these software dependencies as used in their experiments. |
| Experiment Setup | Yes | We tune the hyperparameter ν, and the other hyperparameters follow those used in the baselines. In addition, we incorporate the Sharpness-Aware Minimization (SAM) technique [28]... Algorithm 1 details: if t < T0 then Set α = 1, βy, y Adjust logits during the initial phase ... else Set αy π ν y , βy = 1, y, ν > 0 TLA and ADRW ... Θ Θ η ΘL(f, B) One SGD step Optional: anneal the learning rate η. Required when t = T0. ... Figure 3: (a) Training accuracy of CE+DRW (T0 = 160) |