5 accepted papers
This work proposes the dense adversarial transfer learning based on class-invariance, which is a novel, unsupervised, conditional adversarial domain adaptation approach. The proposed framework concatenates feature maps from the last layer of each backbone’s block to improve transfer learning; these…
Traditional speech enhancement techniques modify the magnitude of a speech in time-frequency domain, and use the phase of a noisy speech to resynthesize a time domain speech. This work proposes a complex-valued Gaussian process latent variable model (CGPLVM) to enhance directly the complex-valued no…
Learning a low-dimensional image representation yields effective and efficient face recognition. The use of such a representation helps to weaken the curse of dimensionality. However, the traditional facial representation method is not robust against partial occlusions or variations of expression. T…
This paper presents an image representation approach which is based on matrix factorization in the complex domain and called exemplar-embed complex matrix factorization (EE-CMF). The proposed EE-CMF approach can very effectively improve the performance of facial expression recognition. Moreover, Wir…
Deep neural network (DNN) have become a popular means of separating a target source from a mixed signal. Most of DNN-based methods modify only the magnitude spectrum of the mixture. The phase spectrum is left unchanged, which is inherent in the short-time Fourier transform (STFT) coefficients of the…