Snapshot Ensembles: Train 1, get M for free

Snapshot Ensembles: A Unique Method to Create Strong Learners

The use of deep neural networks has become increasingly popular in recent years owing to their ability to solve complex problems. However, training multiple deep neural networks can be very expensive in terms of time, hardware, and computational resources. This cost often poses a significant barrier to creating deep ensembles, which are groups of multiple neural networks trained on the same dataset. To overcome this obstacle, Huang and his team proposed a unique method called snapshot ensembles that allows the creation of ensembles at the cost of training only one model.

Core Idea Behind Snapshot Ensembles

The core idea behind snapshot ensembles is to make the model converge to several local minima along the optimization path and store the model parameters a these local minima points. During the training phase, a neural network would traverse through many such points, and the lowest of all such local minima is known as the global minima. For larger models, more local minima points exist, and there are discrete sets of weights and biases at which the model is making fewer errors. Every potential local minimum can effectively be considered a weak but potential learner model for the problem being solved. Multiple snapshots of weights and biases are recorded, which can later be ensembled to create a better generalized model that makes fewer mistakes than a single model.

Advantages of Snapshot Ensembles

  • Reduced Training Time and Computational Resources: The primary advantage of snapshot ensembles is that they can reduce the overhead cost of training multiple deep neural networks. The training time, hardware, and computational resource requirement are decreased since creating snapshots cost less than training multiple independent models.
  • Improved Generalization Ability: Snapshot ensembles yield an ensembled model that has better generalization abilities than independent models. This is because the ensemble combines different and diverse models, making it more robust against overfitting to the training data.
  • Higher Accuracy: By combining multiple models, snapshot ensembles can improve the accuracy of the prediction.
  • Can Be Used with Any Loss Function and Network Architecture : Snapshot ensembles can be used with any loss function and network architecture. This makes it a flexible and portable method applicable to various neural network-based problems.

How Snapshot Ensembles Work?

The basic idea behind snapshot ensembles is to train a single neural network model with a cyclic learning rate schedule. The learning rate schedule involves gradually changing the learning rate of the optimization algorithm, allowing it to oscillate between low and high values during training. The oscillation helps the model converge to different local minima regions, improving the chances of discovering a better set of weights and biases.

After the model is trained until convergence, the training is restarted with a lower learning rate to find a new set of weights closer to another local minima point. This procedure is repeated multiple times until several snapshots of weights and biases have been generated. The saved snapshots can then be saved and later ensembled to create a stronger model.

Snapshot ensembles is a unique and efficient method for creating strong learners. By utilizing the idea of cyclic learning rate schedules, it can save the time, hardware and computational resource requirement, which could be very high in creating deep ensembles. With snapshot ensembles, higher accuracy of predictions, improved generalization ability, and a flexible method applicable to various neural network-based problems can be achieved. This method can be very helpful in various domains such as computer vision, natural language processing, speech recognition, and many more.

Great! Next, complete checkout for full access to SERP AI.
Welcome back! You've successfully signed in.
You've successfully subscribed to SERP AI.
Success! Your account is fully activated, you now have access to all content.
Success! Your billing info has been updated.
Your billing was not updated.