Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual Manifolds.

To address the challenges of long-tailed classification, researchers have proposed several approaches to reduce model bias, most of which assume that classes with few samples are weak classes. However, recent studies have shown that tail classes are not always hard to learn, and model bias has been observed on sample-balanced datasets, suggesting the existence of other factors that affect model bias. In this work, we first establish a geometric perspective for analyzing model fairness and then systematically propose a series of geometric measurements for perceptual manifolds in deep neural networks.

Unveiling and Mitigating Generalized Biases of DNNs through the Intrinsic Dimensions of Perceptual Manifolds.

Building fair deep neural networks (DNNs) is a crucial step towards achieving trustworthy artificial intelligence. Delving into deeper factors that affect the fairness of DNNs is paramount and serves as the foundation for mitigating model biases. However, current methods are limited in accurately predicting DNN biases, relying solely on the number of training samples and lacking more precise measurement tools.

Tradeoffs Between Richness and Bias of Augmented Data in Long-Tail Recognition.

In long-tail scenarios, models have a very high demand for high-quality data. Information augmentation, as an important class of data-centric methods, has been proposed to improve model performance by expanding the richness and quantity of samples in tail classes. However, the underlying mechanisms behind the effectiveness of information augmentation methods remain underexplored.

Improved treatment of coking wastewater and higher biodiversity through immobilization of Comamonas sp. ZF-3 supplemented microbial community.

The aim of this study was to investigate the relationship among pollutant removal performance, microbial community structure, and potential gene function of immobilized microorganisms in coking wastewater (CWW) treatment process. The results showed that the immobilized biomass containing strain Comamonas sp. ZF-3 displayed greater resistance to CWW and higher COD, NH4+-N removal efficiency (92%, 60%) than free cells (48%, 7%), meanwhile, the results from GC-MS proved main organic pollutants in CWW including phenolic compounds, heterocyclic compounds, and polycyclic aromatic hydrocarbons were basically removed by immobilized microorganisms.