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Found 10 papers, 4 papers with code
DSAP: Analyzing Bias Through Demographic Comparison of Datasets
Often, these biases can be traced back to the data used for training, where large uncurated datasets have become the norm.
Metrics for Dataset Demographic Bias: A Case Study on Facial Expression Recognition
One of the most prominent types of demographic bias are statistical imbalances in the representation of demographic groups in the datasets.
Multimodal Parameter-Efficient Few-Shot Class Incremental Learning
Few-Shot Class Incremental Learning (FSCIL) is a challenging continual learning task, where limited training examples are available during several learning sessions.
Gender Stereotyping Impact in Facial Expression Recognition
Our findings support the need for a thorough bias analysis of public datasets in problems like FER, where a global balance of demographic representation can still hide other types of bias that harm certain demographic groups.
Facial Expression Recognition
Facial Expression Recognition (FER)
Verification system based on long-range iris and Graph Siamese Neural Networks
Biometric systems represent valid solutions in tasks like user authentication and verification, since they are able to analyze physical and behavioural features with high precision.
Assessing Demographic Bias Transfer from Dataset to Model: A Case Study in Facial Expression Recognition
Of the three metrics proposed, two focus on the representational and stereotypical bias of the dataset, and the third one on the residual bias of the trained model.
Facial Expression Recognition
Facial Expression Recognition (FER)
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Generative Adversarial Networks for Bitcoin Data Augmentation
In fact, setting GAN training parameters is non-trivial and heavily affects the quality of the generated synthetic data.
Do we still need fuzzy classifiers for Small Data in the Era of Big Data?
Our findings show that, although slightly inferior, Big Data classifiers are gradually catching up with state-of-the-art classifiers for Small data, suggesting that a unified learning algorithm for Big and Small Data might be possible.
On the usage of the probability integral transform to reduce the complexity of multi-way fuzzy decision trees in Big Data classification problems
We present a new distributed fuzzy partitioning method to reduce the complexity of multi-way fuzzy decision trees in Big Data classification problems.
CFM-BD: a distributed rule induction algorithm for building Compact Fuzzy Models in Big Data classification problems
However, when it comes to Big Data classification problems, fuzzy rule-based classifiers have not been able to maintain the good trade-off between accuracy and interpretability that has characterized these techniques in non-Big Data environments.
