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: In 2017, in addition to reformulating softmax loss into an angular/cosine-margin-based loss as mentioned above, some works tries to normalize the features and weights in loss functions to improve the model performance, which can be written as follows:\hat{W}=\frac{W}{\left\|W\right\|},\hat{x}=\alpha\frac{x}{\left\|x\right\|}(6)where \alpha is a scaling parameter, x is the learned feature vector, W is weight of last fully connected layer. Scaling x to a fixed radius \alpha is important, as Wang et al. [110] proved that normalizing both features and weights to 1 will make the softmax loss become trapped at a very high value on the training set. After that, the loss function, e.g. softmax, can be performed using the normalized features and weights.  Some papers [84, 108] first normalized the weights only and then added angular/cosine margin into loss functions to make the learned features be discriminative. In contrast, some works, such as [109, 111], adopted feature normalization only to overcome the bias to the sample distribution of the softmax. Based on the observation of [125] that the L2-norm of features learned using the softmax loss is informative of the quality of the face, L2-softmax [109] enforced all the features to have the same L2-norm by feature normalization such that similar attention is given to good quality frontal faces and blurry faces with extreme pose. Rather than scaling x to the parameter \alpha, Hasnat et al. [111] normalized features with \hat{x}=\frac{x-\mu}{\sqrt{\sigma^{2}}}, where \mu and \sigma^{2} are the mean and variance. Ring loss [117] encouraged the norm of samples being value R (a learned parameter) rather than explicit enforcing through a hard normalization operation. Moreover, normalizing both features and weights [110, 112, 115, 105, 106] has become a common strategy. Wang et al. [110] explained the necessity of this normalization operation from both analytic and geometric perspectives. After normalizing features and weights, CoCo loss [112] optimized the cosine distance among data features, and Hasnat et al. [115] used the von Mises-Fisher (vMF) mixture model as the theoretical basis to develop a novel vMF mixture loss and its corresponding vMF deep features.

Question: How does normalizing the features and weights in the softmax loss function improve the performance of deep face recognition systems?
Normalizing the weights only can help angular/cosine-margin-based loss to make the learned features more discriminative, whereas normalizing only the learned features can help overcome the bias to the sample distribution of the softmax. Since L2-norms of learned features with softmax loss were observed to be reflective of the quality of the face, making all the features have the same L2-norm may help to give similar attention to all different qualities of samples. Lastly, normalizing both the weights and features has become a common method since it was proven necessary by Wang et al. It is difficult to give more specific reasons why normalizing helps softmax loss, as most of the explanations come from referenced papers.