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..
Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective
Authors: Zhengzhuo Xu, Zenghao Chai, Chun Yuan
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments verify that our strategies contribute to a better-calibrated model and their combination achieves state-of-the-art performance on CIFAR-LT, Image Net-LT, and i Naturalist 2018. |
| Researcher Affiliation | Academia | Zhengzhuo Xu1 , Zenghao Chai1 , Chun Yuan1,2 1Shenzhen International Graduate School, Tsinghua University 2Peng Cheng Laboratory |
| Pseudocode | Yes | Algorithm 1 Integrated training manner towards calibrated model. |
| Open Source Code | No | The paper does not contain an explicit statement or link providing open-source code for the described methodology. |
| Open Datasets | Yes | The imbalanced datasets CIFAR-10-LT and CIFAR-100-LT are constructed via suitably discarding training samples following previous works [64, 12, 44, 6]. |
| Dataset Splits | Yes | In our experiment, we utilize the balanced validation set constructed by Cui et al. [12] for fair comparisons. The official splits of train and validation images [6, 64, 30] are adopted for fair comparisons. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not explicitly list software dependencies with specific version numbers. |
| Experiment Setup | Yes | Input: Dtrain, Batch Size N, Stop Steps T1, T2, Random Sampler R, Uni Mix Sampler R Output: Optimized , i.e., feature extractor parameters , classi๏ฌer parameters W , b 1: Initialize the parameters (0) randomly and calculate By via Eq.14 2: for t = 0 to T1 do 3: Sample a mini-batch B = {xi, yi}N i=1 R(Dtrain, N) 4: Sample a mini-batch B = {x j=1 R (Dtrain, N) 5: Calculate Uni Mix factor via Eq.6 6: Construct VRM dataset B = {exk, eyk}N k=1 via Eq.7 7: Calculate LB = E[ i,j LB(yi, (ex; (t))) + (1 j , (ex; (t)))] via Eq.11,15 8: Update (t+1) (t) r (t)LB 9: end for 10: for t = T1 to T2 do 11: Sample a mini-batch B = {xi, yi}N i=1 R(Dtrain, N) 12: Calculate LB = E[LB(yi, (xi; ))] via Eq.15 13: Update (t+1) (t) r (t)LB 14: end for |