Long-Tailed Learning as Multi-Objective Optimization
Authors: Weiqi Li, Fan Lyu, Fanhua Shang, Liang Wan, Wei Feng
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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. |