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 Recognition by Routing Diverse Distribution-Aware Experts
Authors: Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, Stella Yu
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We propose a new long-tailed classifier called Rout Ing Diverse Experts (RIDE). It reduces the model variance with multiple experts, reduces the model bias with a distribution-aware diversity loss, reduces the computational cost with a dynamic expert routing module. RIDE outperforms the state-of-the-art by 5% to 7% on CIFAR100-LT, Image Net-LT and i Naturalist 2018 benchmarks. It is also a universal framework that is applicable to various backbone networks, long-tailed algorithms, and training mechanisms for consistent performance gains. Our code is available at: https://github.com/frank-xwang/RIDE-Long Tail Recognition. |
| Researcher Affiliation | Academia | Xudong Wang1, Long Lian1, Zhongqi Miao1, Ziwei Liu2, Stella X. Yu1 1UC Berkeley / ICSI, 2Nanyang Technological University EMAIL EMAIL |
| Pseudocode | No | The paper describes the method using figures and text but does not include a formal pseudocode or algorithm block. |
| Open Source Code | Yes | Our code is available at: https://github.com/frank-xwang/RIDE-Long Tail Recognition. |
| Open Datasets | Yes | 1. CIFAR100-LT (Cao et al., 2019): CIFAR100 is sampled by class per an exponential decay across classes. We choose imbalance factor 100 and Res Net-32 (He et al., 2016) backbone. 2. Image Net-LT (Liu et al., 2019): Multiple backbone networks are experimented on Image Net LT... 3. i Naturalist 2018 (Van Horn et al., 2018): It is a naturally imbalanced fine-grained dataset with 8,142 categories. |
| Dataset Splits | Yes | The original version of CIFAR-100 contains 50,000 images on training set and 10,000 images on validation set with 100 categories. |
| Hardware Specification | Yes | All backbone networks are trained with a batch size of 256 on 8 RTX 2080Ti GPUs for 100 epochs |
| Software Dependencies | No | The paper mentions optimizers (SGD) and backbone networks (ResNet-32) but does not specify software dependencies with version numbers (e.g., Python, PyTorch/TensorFlow versions). |
| Experiment Setup | Yes | CIFAR100-LT is trained for 200 epochs with standard data augmentations (He et al., 2016) and a batch size of 128 on one RTX 2080Ti GPU. The learning rate is initialized as 0.1 and decayed by 0.01 at epoch 120 and 160 respectively. |