Self-Supervised Learning With Confidence-Weighted Augmented Images
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Solution Overview
Problem
Existing self-supervised learning methods do not account for image uncertainty introduced by data augmentation, which can adversely affect learning accuracy.
Innovation Solution
A self-supervised representation learning method that uses two neural networks to output parameters of probability distributions from augmented image data, optimizing an objective function that includes the likelihood of these distributions to bring the image data closer together, thereby accounting for image uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data augmentation is applied to obtain different image data for self-supervised learning, then the quantity of training data is increased, but image uncertainty is introduced which adversely affects learning accuracy
Solution Approach 1:
A confidence estimation module is introduced as an intermediary between the neural network and the loss calculation. This module estimates the confidence level of each augmented image and uses it to weight the contribution of different samples to the loss function, thereby filtering out low-quality augmented images that would otherwise degrade learning accuracy
Solution Approach 2:
The patent changes the parameter weighting in the loss function by introducing confidence scores. Instead of treating all augmented images equally, the loss function is modified to multiply each sample's contribution by its estimated confidence level, dynamically adjusting the importance of different training samples based on their quality
2Ease of operation
If all augmented images are used equally in training, then the training process is simple, but learning accuracy deteriorates due to inclusion of uncertain images
Solution Approach 1:
A confidence estimation module is introduced as an intermediary between the neural network and the loss calculation. This module estimates the confidence level of each augmented image and uses it to weight the contribution of different samples to the loss function, thereby filtering out low-quality augmented images that would otherwise degrade learning accuracy
Solution Approach 2:
The system performs self-evaluation by having the neural network itself estimate the confidence level of its own predictions on augmented images. The confidence estimation module leverages the network's internal state (such as softmax probabilities or uncertainty estimates) to automatically identify high-quality training samples without requiring external verification
Data Source
AI summary
A self-supervised representation learning method includes: outputting, using one of two neural networks, a first parameter that is a parameter of a probability distribution from one of two items of image data obtained by applying data augmentation to one training image obtained from training data; outputting a second parameter that is a parameter of a probability distribution from an other one of the two items of image data, using an other one of the two neural networks; and training the two neural network to optimize an objective function for bringing the two items of image data close to each other, the objective function including a likelihood of the probability distribution of the second parameter.