Posterior Re-calibration for Imbalanced Datasets
Authors: Junjiao Tian, Yen-Cheng Liu, Nathaniel Glaser, Yen-Chang Hsu, Zsolt Kira
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results on six different datasets and five different architectures show state of art accuracy, including on large-scale imbalanced datasets such as i Naturalist for classification and Synthia for semantic segmentation. |
| Researcher Affiliation | Academia | Junjiao Tian Georgia Institute of Technology jtian73@gatech.edu Yen-Cheng Liu Georgia Institute of Technology ycliu@gatech.edu Nathaniel Glaser Georgia Institute of Technology nglaser@gatech.edu Yen-Chang Hsu Georgia Institute of Technology yenchang.hsu@gatech.edu Zsolt Kira Georgia Institute of Technology zkira@gatech.edu |
| Pseudocode | Yes | Algorithm 1: UNO-IC: multi-modal fusion algorithm for prior and likelihood shift |
| Open Source Code | Yes | Please see https://github.com/GT-RIPL/UNO-IC.git for implementation. |
| Open Datasets | Yes | Cifar10, Cifar100, i Naturalist18 [21], Synthia [23] are listed in Table 1, and [21] and [23] are formal citations to the dataset papers. |
| Dataset Splits | Yes | The hyperparameter λ is searched on the validation set. We randomly split the validation set into three subsets and use 3-fold cross validation for tuning and testing our IC method. The hyperparameter λ is tuned on a validation split and the algorithm is tested on a test set. |
| Hardware Specification | No | The paper does not provide specific details on hardware specifications (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not specify software dependencies (e.g., library names with version numbers like PyTorch 1.9, TensorFlow 2.x) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions tuning the hyperparameter λ and provides some values (e.g., "IC λ = 0.4", "UNO-IC λ = 0.4"). However, it does not provide specific values for common experimental setup details like learning rate, batch size, number of epochs, or optimizer settings for the neural network training. |