Hybrid Model-Based / Data-Driven Graph Transform For Image Coding

This paper introduces a novel deep metric learning-based semi-supervised regression (DML-S2R) method for parameter estimation problems. The proposed DML-S2R method aims to mitigate the problems of insufficient amount of labeled samples without collecting any additional sample with a target value. To this end, it is made up of two main steps: i) pairwise similarity modeling with scarce labeled data; and ii) triplet-based metric learning with abundant unlabeled data. The first step aims to model pairwise sample similarities by using a small number of labeled samples. This is achieved by estimating target value differences of labeled samples with a Siamese neural network (SNN). The second step aims to learn a triplet-based metric space when the number of labeled samples is insufficient. This is achieved by employing the SNN of the first step for triplet-based deep metric learning that exploits not only labeled samples but also unlabeled samples. For the end-to-end training of DML-S2R, we investigate an alternate learning strategy for the two steps. Due to this strategy, the encoded information in each step becomes guidance for learning phase of the other step. The experimental results confirm the success of DML-S2R. The code of the proposed method is publicly available at https://git.tu-berlin.de/rsim/DML-S2R.