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..
Long-Tailed Learning as Multi-Objective Optimization
Authors: Weiqi Li, Fan Lyu, Fanhua Shang, Liang Wan, Wei Feng
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, we conduct extensive experiments on commonly used benchmarks in long-tailed learning and demonstrate the superiority of our method over existing SOTA methods. |
| Researcher Affiliation | Academia | 1College of Intelligence and Computing, Tianjin University 2CRIPAC, MAIS, CASIA |
| Pseudocode | Yes | Algorithm 1: Gradient-balanced grouping |
| Open Source Code | Yes | Our code is released at https://github.com/Wicky Lee1998/GBG v1. |
| Open Datasets | Yes | CIFAR10/100-LT. CIFAR10/100-LT are the long-tailed version of CIFAR10/100. Specifically, they are generated by downsampling CIFAR10/100 with different Imbalance Factor (IF) β = Nmax/Nmin where Nmax and Nmin are the instance size of most frequent and least frequent classes in the training set (Cui et al. 2019; Cao et al. 2019). Image Net-LT. Image Net-LT is sampled from vanilla Image Net following a Pareto distribution with the power value α = 6. It contains 115.8K training images of 1,000 categories with Nmax = 1, 280 and Nmin = 5. We use the balanced validation set of vanilla Image Net which contains 50 images per class. i Naturalist 2018. i Naturailist 2018(i Nat) is a large-scale real-world dataset that naturally presents a long-tailed distribution. It consists of 437.5K images from 8,142 classes with β = 512. The validation set contains 24.4K images with 3 images per class to test our method. |
| Dataset Splits | Yes | CIFAR10/100-LT are the long-tailed version of CIFAR10/100. Specifically, they are generated by downsampling CIFAR10/100 with different Imbalance Factor (IF) β = Nmax/Nmin where Nmax and Nmin are the instance size of most frequent and least frequent classes in the training set (Cui et al. 2019; Cao et al. 2019). Image Net-LT. ... We use the balanced validation set of vanilla Image Net which contains 50 images per class. i Naturalist 2018. ... The validation set contains 24.4K images with 3 images per class to test our method. |
| Hardware Specification | Yes | We train all the above models on NVIDIA Ge Force RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions using SGD but does not provide specific software dependency versions (e.g., Python, PyTorch, or CUDA versions). |
| Experiment Setup | Yes | For CIFAR and Image Net-LT, weight decay (wd) is 5e-4 and momentum (m) is 0.9. For i Nat, wd is 1e-4. We set batch size as 256 for all datasets. |