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..
Decoupling Representation and Classifier for Long-Tailed Recognition
Authors: Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, Yannis Kalantidis
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments and set new state-of-the-art performance on common long-tailed benchmarks like Image Net-LT, Places-LT and i Naturalist, showing that it is possible to outperform carefully designed losses, sampling strategies, even complex modules with memory, by using a straightforward approach that decouples representation and classification. |
| Researcher Affiliation | Collaboration | 1Facebook AI, 2National University of Singapore EMAIL,EMAIL,EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | Our code is available at https://github.com/facebookresearch/classifier-balancing. |
| Open Datasets | Yes | We perform extensive experiments on three large-scale long-tailed datasets, including Places-LT (Liu et al., 2019), Image Net-LT (Liu et al., 2019), and i Naturalist 2018 (i Natrualist, 2018). |
| Dataset Splits | Yes | After training on the long-tailed datasets, we evaluate the models on the corresponding balanced test/validation datasets and report the commonly used top-1 accuracy over all classes, denoted as All. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, processor types, or memory amounts) were provided. |
| Software Dependencies | No | We use the Py Torch (Paszke et al., 2017) framework for all experiments. No specific version number for PyTorch or other software dependencies was provided. |
| Experiment Setup | Yes | For all experiements, if not specified, we use SGD optimizer with momentum 0.9, batch size 512, cosine learning rate schedule (Loshchilov & Hutter, 2016) gradually decaying from 0.2 to 0 and image resolution 224 224. In the first representation learning stage, the backbone network is usually trained for 90 epochs. In the second stage, i.e., for retraining a classifier (c RT), we restart the learning rate and train it for 10 epochs while keeping the backbone network fixed. |