Meta Pseudo Labels (MPL) is a machine learning algorithm that has gained popularity in recent years due to its effectiveness in semi-supervised learning. Semi-supervised learning refers to the task of training a model using both labeled and unlabeled data to improve its accuracy. MPL takes this a step further by using the predictions of a model on unlabeled data to generate "pseudo-labels" and then uses these labels to retrain the model. Pseudo Meta Labels The idea behind MPL is simple: if a model is confident in its predictions on unlabeled data, then those predictions can be used as pseudo-labels to train the model further. The process involves two phases: the first phase trains a model on labeled data, and the second phase uses the trained model to predict labels on unlabeled data, which are then used to retrain the model. The process is repeated until the model converges. One of the key advantages of MPL is its ability to leverage large amounts of unlabeled data, whic

 With a pre-trained "teacher" network, teacher-student training is a method for accelerating training and enhancing the convergence of a neural network. It is widely used to train smaller, less expensive networks from more expensive, larger ones since it is both popular and effective. In a previous post , we discussed the concept of Knowlege Distillation as the idea behind the Teacher-Student model. In this post, we'll discuss the fundamentals of teacher-student training, demonstrate how to do it in PyTorch, and examine the results of using this approach. If you're not familiar with softmax cross entropy, our introduction to it might be a helpful pre-read. This is a part of our series on training targets. Main Concept The concept is basic. Start by training a sizable neural network (the teacher) with training data as per normal. Then, build a second, smaller network (the student), and train it to replicate the teacher's outcomes. For instance, teacher preparation

  Artificial intelligence has revolutionized how we interact with the world, from personal assistants to self-driving cars. Deep neural networks, in particular, have driven much of this progress. However, these networks are typically large, complex, and computationally expensive. In some cases, it is not feasible to use these models in real-world applications, especially when deploying to low-powered devices. To solve this problem, researchers have developed a technique known as knowledge distillation , which allows us to compress large neural networks into smaller, faster, and more efficient ones. In this blog post, we will explore the concept of knowledge distillation, its mathematical underpinnings, and its applications. Additionally, we will provide an implementation of knowledge distillation in Keras, one of the most popular deep-learning frameworks. https://neptune.ai/blog/knowledge-distillation What is Knowledge Distillation? Knowledge distillation is a technique used to transf

If you're interested in the latest advances in deep learning for computer vision, you may have heard about DeiT, or the Data-efficient Image Transformer . DeiT is a state-of-the-art model for image classification that achieves impressive accuracy while using fewer training samples than its predecessors. In this blog post, we'll take a closer look at DeiT and how you can implement and fine-tune it in TensorFlow. What is DeiT? DeiT is a model developed by researchers at META AI that builds on the success of the Transformer architecture , which was originally developed for natural language processing tasks. Like the Transformer, DeiT uses self-attention to process input data, allowing it to capture complex relationships between image features. However, DeiT is specifically designed for image classification tasks, and achieves this by using a novel distillation-based training method that enables it to be trained on smaller datasets than previous models. The key innovation behind