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[20] Y. Sun, G. G. Yen, Z. Yi, Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representations, IEEE Transactions on Evolutionary Computation 23 (1) (2019) 89–103 (Feb. 2019). doi:10.1109/TEVC.2018.2808689. VGG-16:使用小卷积滤波器的深层网络比浅层网络取得更好的效果 Res...
“Kiu-net: Towards accurate segmentation of biomedical images using over-complete representations,” in roc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent.(MICCAI). Springer, 2020, pp. 363–373.
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[18] H. Lee, R. Grosse, R. Ranganath, and A.Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 609–616. ACM, 2009. ...
[21] DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition 标题:DyGait:利用动态表示实现高性能步态识别 链接:arxiv.org/abs/2303.1495 代码:未开源 [22] Identifying Suspicious Regions of Covid-19 by Abnormality-Sensitive Activation Mapping 标题:利用异常敏感激活图识别新冠肺炎的可疑...
A Comprehensive Survey on Deep Image Composition Abstract 图像合成作为一种常见的图像编辑操作,其目的是从一个图像切割前景并将其粘贴到另一幅图像上,得到合成图像。然而,有许多问题可能会使合成图像不现实。这些问题可以概括为前景和背景之间的不一致,
In machine learning, supervised learning aims for the system to predict the correct answers for each input, on the basis of examples. By contrast, in unsupervised learning the goal is to learn useful representations of each sample such that the similarities and differences among them can be obser...
[18] H. Lee, R. Grosse, R. Ranganath, and A.Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 609–616. ACM, 2009. ...
The proposed architecture can be a starting point for further enhancement in Tamil language that supports various applications in the regional language. At the initial stage, the language model aims to generate vector representations for Tamil text input. On the rear end, the GAN models synthesize ...