Virtual Adversarial Training is an effective regularization technique which has given good results in supervised learning, semi-supervised learning, and unsupervised clustering.
This is a re-post of the original post: https://divamgupta.com/unsupervised-learning/semi-supervised-learning/2019/05/31/introduction-to-virtual-adversarial-training.html
Get the source code used in this post from here
Virtual adversarial training has been used for:
There are several regularization techniques which prevent overfitting and help the model generalize better for unseen examples. Regularization helps the model parameters to be less dependent on the training data. The two most commonly used regularization techniques are Dropout and L1/L2 regularization.
In L1/L2 regularization, we add a loss term which tries to reduce the L1 norm or the L2 norm of the weights matrix. Small value of weights would result in simpler models which are less prone to overfitting. …
This post gives an overview of various deep learning based clustering techniques. I will be explaining the latest advances in unsupervised clustering which achieve state-of-the-art performance by leveraging deep learning.
This is a re-post of the original article: https://divamgupta.com/unsupervised-learning/2019/03/08/an-overview-of-deep-learning-based-clustering-techniques.html
Unsupervised learning is an active field of research and has always been a challenge in deep learning. Finding out meaningful patterns from large datasets without the presence of labels is extremely helpful for many applications. Advances in unsupervised learning are very crucial for artificial general intelligence. Performing unsupervised clustering is equivalent to building a classifier without using labeled samples.
In the past 3–4 years, several papers have improved unsupervised clustering performance by leveraging deep learning. Several models achieve more than 96% accuracy on MNIST dataset without using a single labeled datapoint. However, we are still very far away from getting good accuracy for harder datasets such as CIFAR-10 and ImageNet. …