  Deep learning has become a crucial technology for making breakthroughs in
many fields. Nevertheless, it still faces two important challenges in
theoretical and applied aspects. The first lies in the shortcomings of gradient
descent based learning schemes which are time-consuming and difficult to
determine the learning control hyperparameters. Next, the architectural design
of the model is usually tricky. In this paper, we propose a semi-adaptive
synergetic two-way pseudoinverse learning system, wherein each subsystem
encompasses forward learning, backward learning, and feature concatenation
modules. The whole system is trained using a non-gradient descent learning
algorithm. It simplifies the hyperparameter tuning while improving the training
efficiency. The architecture of the subsystems is designed using a data-driven
approach that enables automated determination of the depth of the subsystems.
We compare our method with the baselines of mainstream non-gradient descent
based methods and the results demonstrate the effectiveness of our proposed
method. The source code for this paper is available at
http://github.com/B-berrypie/Semi-adaptive-Synergetic-Two-way-Pseudoinverse-Learning-System}{http://github.com/B-berrypie/Semi-adaptive-Synergetic-Two-way-Pseudoinverse-Learning-System.
