Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance
Authors: Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y Jin, Wenrui Ma, Vidya Muthukumar, Eva L Dyer
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then study this phenomenon in 11 different state-of-the-art pretrained encoders, and show how our proposed framework can be used to compare the quality of encoders, as well as evaluate and combine data augmentation strategies to mitigate the issue. |
| Researcher Affiliation | Collaboration | 1Georgia Institute of Technology, Georgia, the USA. 2Samsung Research. 3Stanford University, California, the USA. |
| Pseudocode | No | The paper describes an ensembling method with numbered steps, but these are presented in paragraph format rather than a formal pseudocode or algorithm block. |
| Open Source Code | Yes | Code can be found at https://github.com/nerdslab/SpectraImbalance. |
| Open Datasets | Yes | For all experiments, we use the standard Image Net ILSVRC 2012 dataset (Deng et al., 2009), which contains C = 1000 object classes with an average of 1281/50 training/validation images per class. |
| Dataset Splits | Yes | For all experiments, we use the standard Image Net ILSVRC 2012 dataset (Deng et al., 2009), which contains C = 1000 object classes with an average of 1281/50 training/validation images per class. |
| Hardware Specification | No | No specific hardware details (e.g., CPU/GPU models, memory) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions software like Torchvision and timm, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In each of the three spectral imbalance settings, we apply Theorem 1 with πy = 0.5, the overparameterization ratio δ = 2, regularization parameter r = 0.5 and the loss as the squared hinge loss, L(t) = max(0, 1 t)2. The scalar min-max problem is solved using gradient descent/ascent with learning rate 0.01. |