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	Online Progressive Instance-Balanced Sampling for Weakly Supervised Vibration Damper Detection 期刊论文 OAI收割 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2023, 卷号: 72, 页码: 14 作者: Chen, Minghao; Tian, Yunong; Li, Zhishuo; Li, En; Liang, Zize \| 收藏 \| 浏览/下载：12/0 \| 提交时间：2023/11/17 Shock absorbers Vibrations Object detection Proposals Training Sampling methods Convolutional neural networks Instance balance multiple instance learning (MIL) progressive sampling vibration damper detection weakly supervised object detection (WSOD)
	DPF-Net: A Dual-Path Progressive Fusion Network for Retinal Vessel Segmentation 期刊论文 OAI收割 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2023, 卷号: 72, 页码: 17 作者: Li, Jianyong; Gao, Ge; Yang, Lei; Bian, Guibin; Liu, Yanhong \| 收藏 \| 浏览/下载：22/0 \| 提交时间：2023/11/17 Feature extraction Image segmentation Retinal vessels Biomedical imaging Blood vessels Convolutional neural networks Task analysis Deep network progressive fusion strategy retinal vessel segmentation semantic segmentation U-shape network
	Progressive polarization based reflection removal via realistic training data generation 期刊论文 OAI收割 PATTERN RECOGNITION, 2022, 卷号: 124, 页码: 13 作者: Pang, Youxin; Yuan, Mengke; Fu, Qiang; Ren, Peiran; Yan, Dong-Ming \| 收藏 \| 浏览/下载：23/0 \| 提交时间：2022/02/16 Deep learning Reflection removal Polarization Progressive network Convolutional neural networks
	Gated Feature Aggregation for Height Estimation From Single Aerial Images 期刊论文 OAI收割 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 卷号: 19, 页码: 5 作者: Xing, Siyuan; Dong, Qiulei; Hu, Zhanyi \| 收藏 \| 浏览/下载：42/0 \| 提交时间：2022/01/27 Estimation Decoding Logic gates Training Feature extraction Testing Encoding Convolutional neural networks (CNNs) gate mechanism height estimation progressive refinement
	Investigation of knowledge transfer approaches to improve the acoustic modeling of Vietnamese ASR system 期刊论文 OAI收割 IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2019, 卷号: 6, 期号: 5, 页码: 1187-1195 作者: Liu, DY (Liu, Danyang)[ 1,2 ]; Xu, J (Xu, Ji)[ 1 ]; Zhang, PY (Zhang, Pengyuan)[ 1,2 ]; Yan, YH (Yan, Yonghong)[ 1,2,3 ] \| 收藏 \| 浏览/下载：19/0 \| 提交时间：2020/01/19 Bottleneck feature (BNF) cross-lingual automatic speech recognition (ASR) progressive neural networks (Prognets) model transfer learning
	Progressive Neural Networks based Features Prediction for the Target Cost in Unit-Selection Speech Synthesizer 会议论文 OAI收割北京, 2018-8 作者: Fu, Ruibo; Tao, Jianhua; Wen, Zhengqi \| 收藏 \| 浏览/下载：41/0 \| 提交时间：2020/06/27 speech synthesis progressive neural networks unit-selection target cost
	Lossless wavelet compression on medical image (EI CONFERENCE) 会议论文 OAI收割 4th International Conference on Photonics and Imaging in Biology and Medicine, September 3, 2005 - September 6, 2005, Tianjin, China 作者: Liu H.; Liu H.; Liu H. 收藏 \| 浏览/下载：41/0 \| 提交时间：2013/03/25 An increasing number of medical imagery is created directly in digital form. Such as Clinical image Archiving and Communication Systems (PACS). as well as telemedicine networks require the storage and transmission of this huge amount of medical image data. Efficient compression of these data is crucial. Several lossless and lossy techniques for the compression of the data have been proposed. Lossless techniques allow exact reconstruction of the original imagery while lossy techniques aim to achieve high compression ratios by allowing some acceptable degradation in the image. Lossless compression does not degrade the image thus facilitating accurate diagnosis of course at the expense of higher bit rates i.e. lower compression ratios. Various methods both for lossy (irreversible) and lossless (reversible) image compression are proposed in the literature. The recent advances in the lossy compression techniques include different methods such as vector quantization wavelet coding neural networks and fractal coding. Although these methods can achieve high compression ratios (of the order 50:1 or even more) they do not allow reconstructing exactly the original version of the input data. Lossless compression techniques permit the perfect reconstruction of the original image but the achievable compression ratios are only of the order 2:1 up to 4:1. In our paper we use a kind of lifting scheme to generate truly loss-less non-linear integer-to-integer wavelet transforms. At the same time we exploit the coding algorithm producing an embedded code has the property that the bits in the bit stream are generated in order of importance so that all the low rate codes are included at the beginning of the bit stream. Typically the encoding process stops when the target bit rate is met. Similarly the decoder can interrupt the decoding process at any point in the bil stream and still reconstruct the image. Therefore a compression scheme generating an embedded code can start sending over the network the coarser version of the image first and continues with the progressive transmission of the refinement details. Experimental results show that our method can get a perfect performance in compression ratio and reconstructive image.