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..
Relative Uncertainty Learning for Facial Expression Recognition
Authors: Yuhang Zhang, Chengrui Wang, Weihong Deng
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that RUL outperforms state-of-the-art FER uncertainty learning methods in both realworld and synthetic noisy FER datasets. |
| Researcher Affiliation | Academia | Yuhang Zhang, Chengrui Wang, Weihong Deng Beijing University of Posts and Telecommunications EMAIL, EMAIL, EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | The code is available at https://github.com/zyh-uaiaaaa/Relative-Uncertainty-Learning. |
| Open Datasets | Yes | RAF-DB [27] is a crowdsourced facial expression dataset that contains 29672 facial images... FER2013 [14] consists of 35,887 grayscale 48x48 pixel images in total... Affect Net [33] is currently the largest FER dataset, including 440,000 images. |
| Dataset Splits | Yes | RAF-DB [27] ... 12271 images as training data and 3068 images as test data. FER2013 [14] ... with 28,709 training samples, 3,589 public test samples, and 3,589 private test samples. Affect Net [33] ... around 280,000 training images and 4000 testing images annotated by human. |
| Hardware Specification | Yes | The model is trained in an end-to-end manner with a single GTX 1080ti GPU for 70 epochs with batch size of 64. |
| Software Dependencies | No | The paper mentions software components like 'Res Net18', 'Adam optimizer', and 'Exponential LR' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We set dropout rate as 0.4, output dimension as 64. The model is trained in an end-to-end manner with a single GTX 1080ti GPU for 70 epochs with batch size of 64. We also utilize an Adam optimizer [24] with weight decay of 0.0001. The learning rate is initialized as 0.0002 except the last fully connected layer for classification, which is 0.002. We use Exponential LR [30] learning rate scheduler with gamma of 0.9 to decrease the learning rate after each epoch. |