# Deep Residual Learning for Image Recognition: A Survey
## 4 Deep Residual Learning for Image Recognition
### Deep Residual Learning Pest Identification  
Deep residual learning is a neural network that is used to learn image representations. It can be used for various tasks such as image classification, object detection, and semantic segmentation. The main advantage of deep residual learning is that it can be trained on very large datasets and achieve good performance with little data. Similarly, Deep Residual Learning for Image Recognition authors studied the effects of applying deep residual learning on pest identification [65]. They found that applying deep residual learning was an effective approach to improving the accuracy rates of identifying pests in images without sacrificing too much speed. Deep residual learning allows the authors to train their models using few iterations. There are three notable approaches for applying deep residual training: dilated convolutions, weight sharing, and weight bias. Dilated convolutions provide better accuracy at lower computation cost than the other two approaches, but this technique has been shown to cause more overfitting when applied at a high level of scale. Weight sharing attempts to reduce computational complexity by not replicating weights across nodes while weight bias attempts to make computations more efficient by replacing fully connected layers with two layers: one containing just weights (which are learned during training) and another containing input features (which are also learned during training). For most models, these two techniques seem more suitable than dilated convolutions because they can still achieve similar accuracies while using less computation time. In addition, researchers have recently developed new architectures based on batch normalization that combine the benefits of both dilated convolutions and weight sharing. One disadvantage to using deep residual learning is that there are limited options for backpropagating errors from higher-level layers to lower-level ones. Additionally, deeper networks tend to suffer from difficulty in generalizing from local examples, which results in problems like mode collapse or overfitting. One way around this problem is to use batch normalization. Researchers found that this increases the degree of freedom for computing gradients, allowing for more accurate predictions [66]. The downside to this technique is that there are multiple variations of algorithms available, so it becomes difficult to know which algorithm will work best for any given task. A survey about deep residual learning showed that there were challenges in trying to implement the technique when real-time inference was required, because certain parts of standard CPUs could not process the large number of multiplications quickly enough. However, many recent research studies have found ways around these issues through hardware optimizations and software solutions such as library bindings. Furthermore, the authors found that deep residual learning outperformed traditional processing for pest identification by 1.4%. This suggests that although there are some limitations to the technique, applying deep residual learning for image recognition can yield significant improvements for users looking to identify pests in images. Deep residual learning can be implemented for image recognition in multiple different ways. Although deep residual learning does result in considerable improvement, there are several significant limitations to the technique. Deep residual learning works best when there is high resolution, large amounts of labeled datasets, and modern hardware that is optimized for implementing deep learning. Finally, it may take a while to optimize the dataset depending on how new the technology is. However, once all of these conditions are met, applying deep residual learning to image recognition should yield significant improvements in accuracy and increase the speed of image recognition significantly.