A survey of mix-based data augmentation: Taxonomy, methods, applications, and explainability
Data augmentation (DA) is indispensable in modern machine learning and deep neural
networks. The basic idea of DA is to construct new training data to improve the model's …
networks. The basic idea of DA is to construct new training data to improve the model's …
Endoood: Uncertainty-aware out-of-distribution detection in capsule endoscopy diagnosis
Being confident in confidence scores: calibration in deep learning models for camera trap image sequences
In ecological studies, machine learning models are increasingly being used for the
automatic processing of camera trap images. Although this automation facilitates and …
automatic processing of camera trap images. Although this automation facilitates and …
On Uncertainty In Natural Language Processing
D Ulmer - arxiv preprint arxiv:2410.03446, 2024 - arxiv.org
The last decade in deep learning has brought on increasingly capable systems that are
deployed on a wide variety of applications. In natural language processing, the field has …
deployed on a wide variety of applications. In natural language processing, the field has …
Revisiting Interpolation Augmentation for Speech-to-Text Generation
Speech-to-text (S2T) generation systems frequently face challenges in low-resource
scenarios, primarily due to the lack of extensive labeled datasets. One emerging solution is …
scenarios, primarily due to the lack of extensive labeled datasets. One emerging solution is …
Uncertainty Quantification Based on Gaussian Processes for Image Segmentation Tasks
B Gao, R Chen, T Yu - 2024 2nd International Conference On …, 2024 - ieeexplore.ieee.org
Over the past several years, deep neural networks have permeated many fields of science
research and have become an essential part of real-world applications. However, when the …
research and have become an essential part of real-world applications. However, when the …
Multiclass Alignment of Confidences and Softened Target Occurrences for Train-time Calibration
V Kugathasan, H Zhou, Z Izzo, G Kuruppu, S Yoon… - openreview.net
In spite of delivering remarkable predictive accuracy across many domains, including
computer vision and medical imaging, Deep Neural Networks (DNNs) are susceptible to …
computer vision and medical imaging, Deep Neural Networks (DNNs) are susceptible to …
Calibration Bottleneck: What Makes Neural Networks less Calibratable?
DB Wang, ML Zhang - openreview.net
While modern deep neural networks have achieved remarkable success, they have
exhibited a notable deficiency in reliably estimating uncertainty. Many existing studies …
exhibited a notable deficiency in reliably estimating uncertainty. Many existing studies …