Results 171 to 180 of about 9,759,453 (201)

Unearthing Gems from Stones: Policy Optimization with Negative Sample Augmentation for LLM Reasoning

Conference on Empirical Methods in Natural Language Processing
Recent advances in reasoning language models have witnessed a paradigm shift from short to long CoT pattern. Given the substantial computational cost of rollouts in long CoT models, maximizing the utility of fixed training datasets becomes crucial.
Zhaohui Yang, Shilei Jiang, Chen Hu, Linjing Li, Shihong Deng, Daxin Jiang   +5 more
semanticscholar   +1 more source

Sample Augmentation and Balance Approach for Improving Classification Performance With High-Resolution Remote Sensed Image

IEEE Geoscience and Remote Sensing Letters
High-resolution remote sensing images can provide detailed information for land cover classification. However, the performance of a classifier depends on the quality and quantity of training samples. Additionally, manually labeling a sufficient number of
ZiQing Zhao, Pei Wang, Pengfei Zhang, Z. Lv   +3 more
semanticscholar   +1 more source

A Sample Augmentation Method for Side-Scan Sonar Full-Class Images That Can Be Used for Detection and Segmentation

IEEE Transactions on Geoscience and Remote Sensing
To solve the problems of small samples, acquisition difficulties, under representation and labeling difficulties in object detection, recognition, and segmentation tasks for underwater all-category targets based on sonar images and deep learning methods,
Zhiwei Yang, Jianhu Zhao, Yongcan Yu, Chao Huang   +3 more
semanticscholar   +1 more source

CNN-Based Multilayer Spatial–Spectral Feature Fusion and Sample Augmentation With Local and Nonlocal Constraints for Hyperspectral Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019
The extraction of joint spatial–spectral features has been proved to improve the classification performance of hyperspectral images (HSIs). Recently, utilizing convolutional neural networks (CNNs) to learn joint spatial–spectral features has become of ...
Jie Feng, Jiantong Chen, Liguo Liu, Xianghai Cao, Xiangrong Zhang, L. Jiao, Tao Yu   +6 more
semanticscholar   +1 more source

CNN Hyperspectral Image Classification Using Training Sample Augmentation with Generative Adversarial Networks

International Conference on Communications, 2020
A big challenge for hyperspectral image recognition is to perform pixel classification when only a few hyperspectral training labeled pixels are available.
V. Neagoe, Paul Diaconescu
semanticscholar   +1 more source

Insulator Breakage Detection Utilizing a Convolutional Neural Network Ensemble Implemented With Small Sample Data Augmentation and Transfer Learning

IEEE Transactions on Power Delivery, 2022
Online fault detection of insulators is a necessary requirement for the development of a smart grid, which directly affects the safety and reliability of power system operations.
Lingcong She, Yadong Fan, Mengxi Xu, Jianguo Wang, Jian Xue, Jianhua Ou   +5 more
semanticscholar   +1 more source

Single-sample augmentation framework for training Viola-Jones classifiers

International Conference on Machine Vision, 2020
In this paper we present a single-sample augmentation framework. The key idea of the framework consists of synthesizing a positive training set from a single natural sample using relevant geometric and pixel intensity transforms.
D. Matalov, S. Usilin, V. Arlazarov
semanticscholar   +1 more source

Sample size of the reference sample in a case‐augmented study

Pharmacoepidemiology and Drug Safety, 2017
AbstractThe case‐augmented study, in which a case sample is augmented with a reference (random) sample from the source population with only covariates information known, is becoming popular in different areas of applied science such as pharmacovigilance, ecology, and econometrics.
Palash Ghosh, Anup Dewanji
openaire   +2 more sources

RandoMix: a mixed sample data augmentation method with multiple mixed modes

Multimedia tools and applications, 2022
Data augmentation plays a crucial role in enhancing the robustness and performance of machine learning models across various domains. In this study, we introduce a novel mixed-sample data augmentation method called RandoMix.
Xiaoliang Liu, F. Shen, Jian Zhao, Changhai Nie   +3 more
semanticscholar   +1 more source

Learning Sample-Specific Policies for Sequential Image Augmentation

Proceedings of the 29th ACM International Conference on Multimedia, 2021
This paper presents a policy-driven sequential image augmentation approach for image-related tasks. Our approach applies a sequence of image transformations (e.g., translation, rotation) over a training image, one transformation at a time, with the augmented image from the previous time step treated as the input for the next transformation.
Pu Li, Xiaobai Liu, Xiaohui Xie
openaire   +1 more source