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: The experiment results are shown in Table 1, the mixed attention has the best performance. Previous works normally focus on only one type of attention, for example scale attention [3] or spatial attention [17], which puts additional constrain on soft mask by weight sharing or normalization. However, as supported by our experiments, making attention change adaptively with features without additional constraint leads to the best performance. We train these networks using different mechanisms and summarize the results in the Table 3.As shown in Table 3, the networks trained using attention residual learning technique consistently outperform the networks trained with baseline method which proves the effectiveness of our method.The performance increases with the number of Attention Module when applying attention residual learning. In contrast, the performance of networks trained with “naive attention learning” method suffers obvious degradation with increased number of Attention Module. We compare our Attention-92 evaluated using single crop on the ILSVRC 2012 validation set with state-of-the-art algorithms.Table 7 shows the results.Our Attention-92 outperforms ResNet-200 with a large margin. The reduction on top-1 error is 0.6\%.Note that the ResNet-200 network contains 32\% more parameters than Attention-92.The computational complexity of Attention-92 shown in the Table 7 suggests that our network reduces nearly half training time comparing with ResNet-200 by adding attention mechanism and reducing trunk depth.Above results suggest that our model enjoys high efficiency and good performance.

Question: Which specific metrics are improved when increasing attention modules ?
The Top-1 and Top-5 error metrics are improved when increasing attention modules.