Real-world data typically follows a long-tailed distribution. When a small sample of tail classes does not cover the underlying distribution well, methods such as class re-balancing strategies and decoupled training are difficult to work, and additional knowledge needs to be introduced to recover the underlying distribution of the tail classes. In this work, we observe that the similarity between the variances of the feature distributions increases with the class similarity. Then, we also find that well-represented feature distributions typically contain multiple subcenters, which allows for denser samples at the edges of the distribution and promotes model learning to more robust decision bounds. Based on these observations, we propose to calibrate the feature distribution of the tail class by transferring the variance of the feature distribution of the head class, and then sample from the calibrated tail class distribution to generate augmented samples. To coordinate with the tail class calibration method, we also propose label-aware noise suppression (LANS) for reducing the generation of noisy samples and a three-stage training scheme for reshaping decision boundaries and compacting feature learning. Experimental results on iNaturalist2018, ImageNet-LT, CIFAR-10-LT, and CIFAR-100-LT show that our method achieves state-of-the-art performance in most metrics compared to similar approaches.

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. Subsequently, we comprehensively explore the effect of the geometric characteristics of perceptual manifolds on classification difficulty and how learning shapes the geometric characteristics of perceptual manifolds. An unanticipated finding is that the correlation between the class accuracy and the separation degree of perceptual manifolds gradually decreases during training, while the negative correlation with the curvature gradually increases, implying that curvature imbalance leads to model bias. We thoroughly validate this finding across multiple networks and datasets, providing a solid experimental foundation for future research. We also investigate the convergence consistency between the loss function and curvature imbalance, demonstrating the lack of curvature constraints in existing optimization objectives. Building upon these observations, we propose curvature regularization to facilitate the model to learn curvature-balanced and flatter perceptual manifolds. Evaluations on multiple long-tailed and non-long-tailed datasets show the excellent performance and exciting generality of our approach, especially in achieving significant performance improvements based on current state-of-the-art techniques. Our work opens up a geometric analysis perspective on model bias and reminds researchers to pay attention to model bias on non-long-tailed and even sample-balanced datasets.

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. Here, we establish a geometric perspective for analyzing the fairness of DNNs, comprehensively exploring how DNNs internally shape the intrinsic geometric characteristics of datasets-the intrinsic dimensions (IDs) of perceptual manifolds, and the impact of IDs on the fairness of DNNs. Based on multiple findings, we propose Intrinsic Dimension Regularization (IDR), which enhances the fairness and performance of models by promoting the learning of concise and ID-balanced class perceptual manifolds. In various image recognition benchmark tests, IDR significantly mitigates model bias while improving its performance.

In scenarios with long-tailed distributions, the model's ability to identify tail classes is limited due to the under-representation of tail samples. Class rebalancing, information augmentation, and other techniques have been proposed to facilitate models to learn the potential distribution of tail classes. The disadvantage is that these methods generally pursue models with balanced class accuracy on the data manifold, while ignoring the ability of the model to resist interference. By constructing noisy data manifold, we found that the robustness of models trained on unbalanced data has a long-tail phenomenon. That is, even if the class accuracy is balanced on the data domain, it still has bias on the noisy data manifold. However, existing methods cannot effectively mitigate the above phenomenon, which makes the model vulnerable in long-tailed scenarios. In this work, we propose an Orthogonal Uncertainty Representation (hOUR) of feature embedding and an end-to-end training strategy to improve the long-tail phenomenon of model robustness. As a general enhancement tool, OUR has excellent compatibility with other methods and does not require additional data generation, ensuring fast and efficient training. Comprehensive evaluations on long-tailed datasets show that our method significantly improves the long-tail phenomenon of robustness, bringing consistent performance gains to other long-tailed learning methods.

Autism spectrum disorder (ASD) is a congenital neurodevelopmental disorder caused by a disorder of the nervous system. ASD is mainly characterized by developmental disorders, as well as abnormalities in social skills, communication skills, interests, and behavioral patterns. ASD is a congenital developmental disorder that cannot be completely cured by existing medical methods, and only after-day intervention can relieve its symptoms. In addition, the sooner you get the intervention, the better the patient's recovery. However, when most patients with ASD show the symptoms, they have missed the best intervention period. In order to allow the subject to be diagnosed earlier with ASD, we applied deep learning techniques to evaluate neural networks in ASD under functional magnetic resonance imaging (fMRI) imaging. At the same time, we also compared the accuracy of detection under the principle of structural magnetic resonance imaging (sMRI) imaging. Our experiments show that neural networks perform much better on fMRI data than sMRI data.