Deep Image Classifier Conformalization Procedure
Published on December 5, 2022, at https://www.paltmeyer.com
In this article, we delve into the process of conformalizing a deep learning image classifier using the Julia ConformalPrediction.jl package. While there are no direct search results specifically describing this process, we can provide a clear, informed explanation based on general principles of conformal prediction applied to classifiers and the Julia ecosystem.
To conformalize a deep learning image classifier in Julia, follow these steps:
- Train your deep learning model on your image data. For example, using Flux.jl or another Julia deep learning framework, train a classifier that outputs scores or probabilities over classes.
- Obtain calibration data that is separate from your training set. Conformal prediction works by using a calibration set to compute nonconformity scores.
- Define a nonconformity measure. For classification, a common choice is to use 1 minus the predicted probability of the true class (or the softmax confidence score) as the nonconformity score. This quantifies how “strange” or “nonconforming” a prediction is.
- Compute nonconformity scores for the calibration set. For each calibration example, run the classifier to get predicted scores and compute the nonconformity score.
- Use the Conformal Prediction package to compute prediction sets. Load the package (e.g., ), use its functions to calculate quantiles of the calibration nonconformity scores, defining a threshold for a chosen confidence level (1 - \alpha). When you apply the classifier to a new image, create a prediction set of classes whose nonconformity score is below this threshold.
- Output the conformal prediction set, which contains all classes for the new instance that are plausible at the desired confidence level. This set adjusts for uncertainty and provides formal coverage guarantees.
This process “wraps” your deep learning classifier with conformal prediction, turning point predictions into calibrated prediction sets.
Notes specific to Julia and image classifiers:
- Julia deep learning is often done with Flux.jl or Knet.jl.
- The ConformalPrediction.jl package provides tools for classification conformal methods (split conformal/inductive conformal).
- You may need to adapt your model’s output to the expected interface for the package (e.g., obtaining class probabilities).
- The package usually provides examples for tabular data but can be adapted straightforwardly for image classifiers.
Summary of workflow in Julia pseudocode:
```julia using Flux using ConformalPrediction
model = train_image_classifier(...)
nonconformity_scores = [ 1 - model(image)[label] for (image, label) in zip(calibration_images, calibration_labels) ]
conf_pred = ConformalClassifier(nonconformity_scores, alpha=0.1)
image_new = ... prediction_set = conf_pred.predict_set(model(image_new)) ```
This approach ensures you get a set of plausible classes with formal guarantees that the true class is contained in the set with probability at least (1 - \alpha).
By following these steps, you can successfully conformalize a deep learning image classifier in Julia, providing more robust and reliable predictions for your machine learning tasks.
- To leverage data-and-cloud-computing, train and conformalize your deep learning image classifier in Julia using cloud-based computing services, allowing for faster training times and scalability.
- The technology of artificial-intelligence, when combined with the conformalization of deep learning image classifiers in Julia, can lead to more reliable and robust AI systems, offering improved performance and reduced uncertainty in prediction outcomes.
