Sparse Quantized Spectral ClusteringZhenyu LiaoRomain CouilletMichael W. MahoneyInternational Conference on Learning Representations
(2021), the authors applied four detection methods on medical DNNs called Kernel density (KD) (Feinman, Curtin, Shintre, & Gardner, 2017), local intrinsic dimensionality (LID) (Ma, et al., 2018), Deep features (DFeat) and quantized features (QFeat) (Lu, Issaranon, & Forsyth, 2017)...
This in-path spectral masking usually results in sparse vectors. The sparse frequency domain signal is itself quantized and encoded in the output bit-stream. The ASVQ scheme used by the invention is a vector quantization algorithm that is particularly effective for quantizing sparse signal vectors...
Figure 2. Quantized spectral maps. Because Markov chains take categorical values, the CWT must be quantized to levels. (a) The target transiogram extracted from training datasets (X represented time point, Y represented frequency). (b) Imputation bands specified how MCRF performed for each freque...
Then the descriptors were subsequently quantized into a vocabulary of 600 visual words that were generated by k-means clustering. The PhowColor is the color version of PhowGray that stacks SIFT descriptors for each HSV color channel. Self-SIMilarity features (SSIM). SSIM descriptors [12] were ...
Then the descriptors were subsequently quantized into a vocabulary of 600 visual words that were generated by k-means clustering. The PhowColor is the color version of PhowGray that stacks SIFT descriptors for each HSV color channel. Self-SIMilarity features (SSIM). SSIM descriptors [12] were ...