You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: However, we argue that increasing depth significantly boosts performance. We successfully use 20 weight layers (3\times 3 for each layer). Our network is very deep (20 vs. 3 [6]) and information used for reconstruction (receptive field) is much larger (41\times 41 vs. 13\times 13). In this section, we study three properties of our proposed method. First, we show that large depth is necessary for the task of SR. A very deep network utilizes more contextual information in an image and models complex functions with many nonlinear layers. We experimentally verify that deeper networks give better performances than shallow ones. We now experimentally show that very deep networks significantly improve SR performance. We train and test networks of depth ranging from 5 to 20 (only counting weight layers excluding nonlinearity layers). In Figure 3, we show the results. In most cases, performance increases as depth increases. As depth increases, performance improves rapidly. In this work, we have presented a super-resolution method using very deep networks. Training a very deep network is hard due to a slow convergence rate. We use residual-learning and extremely high learning rates to optimize a very deep network fast. Convergence speed is maximized and we use gradient clipping to ensure the training stability. We have demonstrated that our method outperforms the existing method by a large margin on benchmarked images. We believe our approach is readily applicable to other image restoration problems such as denoising and compression artifact removal.

Question: Did increasing the network depth improved the results?
Yes, it did boost performance.