Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Enhancing Class-Imbalanced Learning with Pre-Trained Guidance through Class-Conditional Knowledge Distillation
Authors: Lan Li, Xin-Chun Li, Han-Jia Ye, De-Chuan Zhan
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on various imbalanced datasets demonstrate an average accuracy improvement of 7.4% using our method. |
| Researcher Affiliation | Academia | 1School of Artificial Intelligence, Nanjing University, China 2National Key Laboratory for Novel Software Technology, Nanjing University, China. |
| Pseudocode | Yes | Appendix B. Pseudo Code of ACCKD. Algorithm 1 Augmented Class-Conditional Knowledge Distillation (ACCKD) |
| Open Source Code | Yes | Our code is available at https://github.com/Lain810/CCKD. |
| Open Datasets | Yes | Dataset. We performed extensive experiments on three widely used imbalanced datasets: CIFAR-100-LT (Cui et al., 2019), Image Net-LT (Russakovsky et al., 2015), and i Naturalist 2018 (Horn et al., 2018). |
| Dataset Splits | Yes | It should be estimated using a validation set Dval that shares the same distribution as the test data... For CIFAR-100, following Cui et al. (2019), we created class imbalance by the imbalance ratio of Nmax/Nmin = 100. Image Net-LT consists of 115.8K images across 1000 classes, with a maximum of 1280 images and a minimum of 5 images per class. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or specific computing environments) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Res Net-32 and Res Net-18 as backbone networks but does not specify any software libraries or their version numbers (e.g., PyTorch, TensorFlow, CUDA versions) required to reproduce the experiments. |
| Experiment Setup | Yes | We set the batch size to 128 and train for 200 epochs, using SGD as the optimizer. The initial learning rate of the optimizer is set to 0.1, with a momentum of 0.9 and a weight decay of 5 10 4. In terms of hyperparameters, we set the balancing parameters for the loss terms, α1 and α2, to 1 and 2, respectively. The temperature parameter for distillation, τ, is set to 2. |