Dual Supervised Learning
This addresses the inefficiency in handling dual tasks like translation pairs for researchers and practitioners, though it is incremental as it builds on existing supervised learning frameworks.
The paper tackles the problem of training dual supervised learning tasks separately by proposing a method to train them simultaneously and exploit their probabilistic correlation, resulting in improved performance for both tasks across applications like machine translation, image processing, and sentiment analysis.
Many supervised learning tasks are emerged in dual forms, e.g., English-to-French translation vs. French-to-English translation, speech recognition vs. text to speech, and image classification vs. image generation. Two dual tasks have intrinsic connections with each other due to the probabilistic correlation between their models. This connection is, however, not effectively utilized today, since people usually train the models of two dual tasks separately and independently. In this work, we propose training the models of two dual tasks simultaneously, and explicitly exploiting the probabilistic correlation between them to regularize the training process. For ease of reference, we call the proposed approach \emph{dual supervised learning}. We demonstrate that dual supervised learning can improve the practical performances of both tasks, for various applications including machine translation, image processing, and sentiment analysis.