Augmentation Loss Function for Image Classifier Training
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Solution Overview
Problem
Image classifiers face challenges in accurately classifying images due to inadequate or imbalanced training datasets, particularly when images are rotated, upside-down, or have variations in focus, brightness, contrast, and zoom, leading to differences in model outputs for augmented images.
Innovation Solution
A customized augmentation loss function is introduced to reduce differences in model outputs for different augmentations of the same image during supervised training, using an augmentation loss coefficient to reflect the complexity of each augmentation type, thereby emphasizing salient features and de-emphasizing non-salient ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image classifiers are trained on augmented images with different transformations (rotation, brightness, contrast), then the model's adaptability to various image conditions is improved, but the model output accuracy for different augmentations of the same image deteriorates
Solution Approach 1:
The patent introduces a feedback mechanism through the augmentation loss function that monitors and corrects model output differences between augmented images and their originals. The loss function provides feedback during training to adjust model parameters, ensuring that augmented images are treated similarly to original images while maintaining adaptability to various conditions.
Solution Approach 2:
The patent changes the training parameters by introducing an augmentation loss coefficient that can be adjusted to reflect the complexity of different augmentation types. This allows the model to learn from augmented images while controlling the weight given to different augmentation levels, resolving the contradiction between adaptability and output accuracy.
2Ease of manufacture
If traditional loss functions are used for training image classifiers, then the training process is simple, but the classifier cannot adequately handle image augmentations and their variations
Solution Approach 1:
The patent merges the traditional loss function with an augmentation-specific loss component to create a composite loss function. This combination allows the training process to remain relatively simple while adding the capability to handle image augmentations, improving classification reliability without significantly complicating the training process.
Solution Approach 2:
The augmentation loss function acts as an intermediary between the traditional loss function and the augmented image data. It mediates the training process by introducing augmentation-specific considerations while working with the existing training framework, thereby improving reliability without requiring a complete redesign of the training process.
3Adaptability or versatility
If all augmentation types are treated equally in training, then the training process is uniform, but important salient features may be diluted by non-salient feature variations
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different augmentation types through the augmentation loss coefficient. Instead of uniform treatment, the model assigns different weights to different augmentation levels based on their salience, allowing important features to be emphasized while reducing the impact of non-salient variations.
Solution Approach 2:
The patent uses partial action by selectively applying stronger penalties to certain augmentation types that introduce non-salient variations. The augmentation loss function can be configured to apply different levels of correction based on the specific augmentation applied, preventing over-correction of important features while adequately addressing non-salient ones.
Data Source
AI summary
Described are techniques for training an image classifier using an augmentation loss function. The techniques including inputting corresponding pairs of a plurality of training images to an image classifier, where respective pairs of the corresponding pairs comprise at least two images having a same classification and different augmentations. The techniques further including training an artificial neural network of the image classifier to classify the plurality of training images using an augmentation loss function, wherein the augmentation loss function reduces differences in model outputs between the corresponding pairs of the plurality of training images.