Can a powerful neural network be a teacher for a weaker neural network?
This work addresses a domain-specific problem in neural network training, offering an incremental improvement for knowledge transfer techniques.
The paper tackles the problem of improving a weak neural network's performance by using a more powerful network as a teacher, adding a loss function to minimize feature distance during training. Experiments on three datasets show that this approach effectively increases the weak network's performance, though no specific numerical results are provided.
The transfer learning technique is widely used to learning in one context and applying it to another, i.e. the capacity to apply acquired knowledge and skills to new situations. But is it possible to transfer the learning from a deep neural network to a weaker neural network? Is it possible to improve the performance of a weak neural network using the knowledge acquired by a more powerful neural network? In this work, during the training process of a weak network, we add a loss function that minimizes the distance between the features previously learned from a strong neural network with the features that the weak network must try to learn. To demonstrate the effectiveness and robustness of our approach, we conducted a large number of experiments using three known datasets and demonstrated that a weak neural network can increase its performance if its learning process is driven by a more powerful neural network.