Self-Supervised Contrastive Learning With Hard-Sample Weighting
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Solution Overview
Problem
Existing self-supervised contrastive learning (SSCL) methods face challenges with reduced learning efficiency due to hard positive and negative samples, especially in tasks with high similarity between classes, leading to degraded performance and prolonged learning times, particularly in applications like fashion and clothing classification.
Innovation Solution
The method involves generating multiple view images through conversion schemes, using a backbone and momentum network to generate expression vectors, classifying these into positive and negative samples based on anchor vectors, and updating network parameters via a loss function, effectively addressing hard samples and improving classification performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simple positive and negative sample classification methods are used in SSCL, then the implementation is straightforward, but learning efficiency is reduced due to hard positive and negative samples
Solution Approach 1:
The patent segments the batch of expression vectors into multiple groups based on similarity thresholds. Instead of treating all samples uniformly, it divides them into different categories (e.g., hard positive samples, easy positive samples, hard negative samples, easy negative samples) and applies different loss function weights to each group, thereby improving learning efficiency while maintaining implementation feasibility
Solution Approach 2:
The patent applies different quality standards to different sample groups. By calculating similarity between expression vectors and identifying hard samples (those with unexpectedly high or low similarity), it applies localized adjustments through weighted loss functions specific to each sample type, rather than using a uniform approach across all samples
2Productivity
If large batch size is used to increase learning efficiency, then more samples are processed per iteration, but the number of difficult negative samples increases significantly
Solution Approach 1:
The patent changes the parameter weights in the loss function based on sample characteristics. By dynamically adjusting the weight of each sample's loss contribution based on its similarity to the anchor vector, it can process large batches while maintaining classification accuracy through selective emphasis on more informative samples
3Adaptability or versatility
If existing SSCL methods are applied to high-similarity classes (fashion, clothing, dog subspecies), then the same framework is used, but classification performance is degraded
Solution Approach 1:
The patent introduces dynamic sample selection and weighting mechanisms that adapt to the specific characteristics of the dataset. By continuously identifying hard samples through similarity calculations and adjusting loss weights accordingly, the system adapts to high-similarity class scenarios while maintaining the general SSCL framework
Solution Approach 2:
The patent implements a feedback mechanism where the similarity between expression vectors is calculated and used to identify hard samples, which then receive adjusted weights in the loss function. This feedback loop allows the system to automatically adapt to difficult classification scenarios without requiring task-specific modifications to the overall framework
Data Source
AI summary
Disclosed is a method for self-supervised contrastive learning. The method may include generating a plurality of different view images by applying at least one conversion scheme for contrastive learning to a pre-stored non-index object image, generating expression vectors by alternately inputting the plurality of view images to a backbone network and a momentum network initialized to have an identical network parameter values, classifying the plurality of view images into a positive sample and a negative sample based on an anchor vector selected in a batch of the expression vectors, calculating a loss value of a loss function based on a distance value between the anchor vector and the expression vector, and updating parameters of the backbone network and the momentum network by reversely propagating the loss value of the loss function to the backbone network.