DIME: Detecting Out-of-Distribution Examples with Distance to Modelled Embedding
DIME is a powerful tool in machine learning that helps detect out-of-distribution examples during prediction time. In order to understand what DIME does, we first need to understand what it means to train a neural network and how it works.
When we train a neural network, we feed it a set of training data drawn from some high-dimensional distribution in data space X. The neural network then transforms this training data into the model’s intermediate feature vector space, which can be represented mathematically as R^p. The network's goal is to learn the underlying patterns and relationships in the data and use that knowledge to make accurate predictions.
Once the neural network is trained, it can then be used to make predictions on new observations. However, it can be difficult to assess if these new observations are out-of-distribution directly in data space. That's where DIME comes in.
Understanding Distance to Modelled Embedding (DIME)
Essentially, DIME is a method that transforms new observations into the same intermediate feature space that the neural network uses, and then uses the Distance-to-Modelled-Embedding (DIME) to assess whether these new observations fit into the expected embedding covariance structure.
So what exactly does that mean?
Well, when we train a neural network, the training set embedding can be linearly approximated as a hyperplane in the intermediate feature space. In other words, we can use a linear model to describe the relationship between the input features and the output we want to predict. This hyperplane is essentially a template that the neural network uses to make predictions.
When we use DIME to assess new observations, we transform these observations into the intermediate feature space and then compare them to the hyperplane. Specifically, we calculate the distance between the new observation and the hyperplane. If this distance is greater than some threshold, we can say that the new observation is out-of-distribution.
The Benefits of DIME
So why is DIME so useful?
First of all, it helps us detect out-of-distribution examples, which can be extremely important in many applications. For example, let's say we're using a neural network to classify images of animals. If an out-of-distribution example, such as a picture of a car or a building, is mistakenly classified as an animal, this could have serious consequences.
Secondly, DIME is a relatively simple and computationally efficient method. It can be easily integrated into existing machine learning pipelines and requires minimal additional training data.
Limitations of DIME
While DIME is a valuable tool for detecting out-of-distribution examples, it does have some limitations.
Firstly, DIME relies on the assumption that the embedding covariance structure is linearly approximated by a hyperplane. In reality, this may not always be the case, and the hyperplane may not accurately capture the relationship between the input features and the output.
Secondly, DIME can only detect out-of-distribution examples that are far away from the hyperplane. It may not be able to detect examples that are close to the hyperplane but still outside of the expected distribution.
Distance to Modelled Embedding (DIME) is a valuable tool in machine learning that helps detect out-of-distribution examples during prediction time. By transforming new observations into the same intermediate feature space that the neural network uses, and then assessing the distance to the hyperplane, we can determine whether these observations fit into the expected embedding covariance structure. While DIME has its limitations, it is a relatively simple and computationally efficient method that can be easily integrated into existing machine learning pipelines.
