Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective
Authors: Zhengzhuo Xu, Zenghao Chai, Chun Yuan
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 , classifier 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 |