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While the traditional data augmentation like those provided in Keras ImageDataGenerator class consistently leads to improved generalization, the procedure is dataset-dependent, and thus requires the use of expert knowledge.īesides, data augmentation does not model the relation across examples of different classes. Α ∈ leads to improved performance, smaller α creates less mixup effect, whereas, for large α, mixup leads to underfitting.Īs you can see in the following graph, given a small α = 0.2, beta distribution samples more values closer to either 0 and 1, making the mixup result closer to either one of the two examples. ( x i y i ) and ( x j y j ) are two examples drawn at random from our training data, and λ ∈ , in practice, λ is randomly sampled from the beta distribution, i.e. B y forming a new example through weighted linear interpolation of two existing examples. The paper mixup : B EYOND E MPIRICAL R ISK M INIMIZATION offers an alternative to traditional image augmentation technique like zooming and rotation. For large datasets we can generate unique transformed images for every batch of an epoch.Previously, we introduced a bag of tricks to improve image classification performance with convolutional networks in Keras, this time, we will take a closer look at the last trick called mixup. At test time we use the test image directly without any transformations.įor small datasets we can generate the transformations of the images and train the model with all the data at once. We apply this technique only for the training dataset. We can apply this technique at the time of the data generation after preprocessing and before training. In case of imbalanced data we can generate more images for the class which has less data. We can simply use some techniques and generate images which are ten times of our dataset or even more. We need not dig in google for new images. “What do we do if we have less amount of data or imbalance data?” Deep learning models often require more data which is not always available. To accommodate all these parameters we need to have a good amount of data. In order to train a model for accurate results we need to have more number of parameters to learn almost all the features from the data. Most of the state-of-the-art models contain lots of parameters in the order of millions. We can apply data augmentation to different types of data, but in this article we are focusing on the Image Data Augmentation techniques that are used in common. The model should be trained in such a way that it can detect the object accurately irrespective of the above factors. The image might be the left view of the car or the right view.Īll these factors affect the model while evaluating an image. The image may be clicked on a bright sunny day or on a cloudy day. The main reason for this, as we all know the real world data may not always be in the correct form.įor example, consider a car in an image, the car may not be at the center in all cases, sometimes it can be in the left side of the image or right. In other terms, we are artificially increasing the size of the dataset by creating different versions of the existing data from our dataset. In the same way for building deep learning models we use different data augmentation methods to create more meaningful data which can be used for building deep learning models.īelow are the concepts you are going to learn in this article.ĭata Augmentation is a process of increasing the available limited data to large meaningful and more diversity amounts. In machine learning to solve the similar kind of problem handling limited data, we use the oversampling method.
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No right, We need to find ways to use the available data, to generate more data with more diversity. So we will stop building the model in such cases. So we are clear now, we need large amounts of data to build deep learning models but not all the time we will have enough data, If we are not able to feed the right amount of data the deep learning models we build face the underfitting issue, Sometime the data we feed needs to be more diversified, else even if we are feeding high amount data, the model will face the overfitting issue. In particular for deep learning models more data is the key for building high performance models. The machine gets more learning experience from feeding more data. Five Popular Data Augmentation techniques In Deep LearningĪs Alan turing said What we want is a machine that can learn from experience.