In recent studies, alongside introducing new approaches for color constancy, we have also focused on improving existing techniques and introducing new perspectives. Our motivation is the idea that investigating different strategies, concepts, and their combinations that have not been analyzed in this field in detail yet, might help us to find simple, effective, and cost-efficient solutions. Thereupon, we utilized observations we obtained from our algorithms to analyze how we can enhance the performance of well-known learning-free methods. We demonstrated why using salient pixels, performing block-based operations, and carrying out scale-space computations benefit color constancy approaches significantly and provide a notable performance increase. In this study, we make use of our recent observations on learning-free algorithms to analyze if they are also beneficial for enhancing the performance of a learning-based color constancy model. According to our evaluation, all of our observations contribute to the performance of a convolutional neural network model and increase its effectiveness in estimating the illuminant. Thus, the contribution of these strategies in learning-based models should be further investigated to improve their performance with simple yet effective solutions.