Fig. 1.

An illustration of the proposed feature augmentor (FA) as detailed in Section III-A. For each operation, the generated tensor/matrix and the dimensions are marked aside the corresponding arrow. The operations for converting tensors back to matrices are not included for simplification. The augmented features tensor $\mathcal{A}$ contains free parameters and is trained during the whole learning process to capture shared patterns of cleft features. The query tensor $\mathcal{Q}$ is obtained by performing a 1 × 1 × 1 convolution on $\mathcal{A}$. The depth, height, and the width of the output tensor $\mathcal{N}$ are determined by those of $\mathcal{A}$ or $\mathcal{Q}$. These dimensions can be flexibly adapted per design requirements. Hence, the proposed FA can replace any commonly-used operation like pooling or deconvolution, and can be integrated to any deep architecture with high flexibility.