Efficient Multi-Domain Dictionary Learning with GANs
This work addresses domain adaptation in classification for applications like image or signal processing, but it is incremental as it builds on existing dictionary learning and GAN methods.
The paper tackles the problem of making dictionary learning-based classification robust across different data domains by using adversarial neural networks to generate diverse data and compressing it into a single sample per class via a weighting matrix, resulting in maintained time complexity and potentially improved classification accuracy.
In this paper, we propose the multi-domain dictionary learning (MDDL) to make dictionary learning-based classification more robust to data representing in different domains. We use adversarial neural networks to generate data in different styles, and collect all the generated data into a miscellaneous dictionary. To tackle the dictionary learning with many samples, we compute the weighting matrix that compress the miscellaneous dictionary from multi-sample per class to single sample per class. We show that the time complexity solving the proposed MDDL with weighting matrix is the same as solving the dictionary with single sample per class. Moreover, since the weighting matrix could help the solver rely more on the training data, which possibly lie in the same domain with the testing data, the classification could be more accurate.