# Deep Residual Learning for Image Recognition: A Survey
## 6 Advantages of DRN over Other Models  
Deep residual learning has quickly become the go-to method for image recognition tasks. This is because deep residual learning offers a number of advantages over other models, including:  
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2. Ease of training--deep residual networks are easier to train than other models due to their simple structure and use of short connections between layers.
3. Better performance--deep residual networks have been shown to outperform other models on a variety of image recognition tasks.
4. Reduced need for data--deep residual networks can learn from smaller datasets than other models due to their ability to learn features from data with lower dimensional representations.
5. Increased robustness--deep residual networks are more robust to changes in data distribution and model parameters than other models.
6. Lower computational requirements--deep residual networks require less computation than other models.
7. Applications beyond image recognition--as they are computationally cheap, deep residual networks are being applied to a variety of different fields such as natural language processing and audio processing. These properties make deep residual networks an attractive choice for anyone looking to develop an image recognition system. However, there are some disadvantages that should be considered before choosing this technique:  
(1) Higher memory requirements--while deep residual networks typically consume less memory than other models during runtime, they generally take up more space during the training process. (2) Slower convergence time, though not a problem for many applications, may be a limiting property for some computer vision tasks. (3) Additional work needed to account for gradient noise--since deep residual networks rely on back propagation through time (BPTT), extra care must be taken when designing these systems to account for gradient noise that might occur. (4) Training still needs careful parameter tuning--even with all these benefits, there is still room for improvement by carefully selecting appropriate values of hyperparameters during training. Finally, we would like to mention two drawbacks of deep residual networks:  
(1) More complex architecture. Although this isn't necessarily a bad thing, it means that it will take longer for beginners to understand how the system works. For example, many other types of neural networks follow a standard three layer architecture consisting of input layer \(\rightarrow\) hidden layer \(\rightarrow\) output layer. A deep residual network will consist of several additional layers (e.g., input layer \(\rightarrow\) convolutional or deconvolutional layer \(\rightarrow\) RELU/tanh or sigmoid activation function \(\rightarrow\) pooling layer \(\rightarrow\) fully connected layer). The added complexity means that one will have to spend more time understanding how a network is put together before they can start modifying it themselves; (2) It is not yet applicable to all use cases. At the moment, deep residual networks are only useful for image recognition. They cannot be used for speech recognition or text generation. As mentioned earlier, there are many advantages associated with deep residual learning; however, it is important to consider any tradeoffs before deciding whether this technique is suitable for a particular application.