cure算法的matlab代码-Semantic-Autoencoder:该存储库提供了基于稀疏自动编码器权重的语义进行训练和分类的代码

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治愈算法的matlab代码语义可解释和可控制的过滤器集 这是本文的MATLAB实现: ,,和,“语义可解释和可控制的滤波器集”,2018年第25届IEEE国际图像处理会议(ICIP),雅典,2018年,第1053-1057页。 (*:均等) [] 抽象的 在本文中,我们生成和控制语义可解释的过滤器,这些过滤器可以无监督的方式从自然图像中直接学习。 每个语义过滤器都会与其他过滤器一起学习视觉上可解释的局部结构。 学习这些可解释的过滤器集的重要性在两个对比的应用程序中得到了证明。 第一个应用是渐进式脱色下的图像识别,其中识别算法应对颜色不敏感以实现稳定的性能。 第二个应用是图像质量评估,其中客观方法应对颜色退化敏感。 在提出的工作中,通过基于语义过滤器表示的局部结构对语义过滤器进行加权来控制其敏感性和不足。 为了验证所提出的方法,我们利用CURE-TSR数据集进行图像识别,并利用TID 2013数据集进行图像质量评估。 我们表明,提出的语义过滤器集在两个数据集中均实现了最新的性能,同时保持了其在渐进式失真中的鲁棒性。 语义自动编码器 我们研究了不同的正则化技术,包括l 1 ((a),(d)

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