Invertible Latent Factorization for Classifier Data Augmentation
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Solution Overview
Problem
Existing classifier training methods are limited in the variety of invariances they can learn, leading to suboptimal classification accuracy due to reliance on a limited amount of training data and inadequate data augmentation techniques.
Innovation Solution
The use of an invertible factorization model to generate disentangled latent representations, allowing for more comprehensive data augmentation by altering specific factors of the input signal, such as brightness or rotation, and training the classifier to be invariant or equivariant to these variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data augmentation is used to train the classifier with more variations, then classification accuracy is improved, but the amount of training data and computational resources required increases
Solution Approach 1:
The invertible factorization model pre-processes the training data by decomposing it into disentangled latent factors before the classifier training. This preliminary decomposition allows the classifier to learn from structured, factorized representations, reducing the need for large amounts of raw training data while improving classification accuracy through better feature organization.
2Adaptability or versatility
If existing data augmentation approaches are used, then some invariances can be learned, but the variety of invariances that can be learned is limited
Solution Approach 1:
The invertible factorization model segments the training data into distinct latent factors that represent different invariances (e.g., brightness, contrast, rotation). This segmentation allows the classifier to independently learn each type of invariance, significantly expanding the variety of invariances that can be learned compared to traditional augmentation approaches that apply transformations in a limited, non-factorized manner.
Data Source
AI summary
A computer-implemented method for training a classifier. The classifier is configured to determine an output signal characterizing a classification of an input signal. The training of the classifier includes: determining a first training input signal; determining a first latent representation comprising a plurality of factors based on the first training input signal by means of an invertible factorization model, wherein the invertible factorization model, determining a second latent representation by adapting at least one factor of the first latent representation; determining a second training input signal based on the second latent representation by means of the invertible factorization model; and training the classifier based on the second training input signal.