Contrastive Learning Model Weighting by Sample Distance
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Solution Overview
Problem
In contrastive learning models, different construction methods for negative samples result in varying difficulties, affecting the training effectiveness, and there is a need to consider the contribution of negative samples of different difficulties to improve model performance.
Innovation Solution
A method is provided to manage models based on the distance between samples, where a basic sample and associated negative samples are obtained, sequenced by distance, divided into sets of differing difficulty, and updated parameters are determined using weights that reflect the difficulty of each set of negative samples, with the first set being more similar to the basic sample and thus more challenging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a large number of negative samples are constructed to improve model performance, then the training data quantity increases, but the training effectiveness varies due to different difficulties of negative samples
Solution Approach 1:
The patent applies local quality by differentiating negative samples into multiple sets based on their difficulty levels. Instead of treating all negative samples uniformly, the method categorizes them into first set (easier) and second set (harder) negative samples, assigning different weights to each set during training. This allows the model to focus on difficult samples while still benefiting from easier ones, thereby improving overall training effectiveness.
Solution Approach 2:
The patent segments the negative samples into distinct groups based on their distance from the basic sample. By dividing the sequence of negative samples into multiple sets with different difficulty levels, the method enables targeted training strategies for each segment, addressing the variability in training effectiveness mentioned in the contradiction.
2Device complexity
If negative samples of different difficulties are treated equally, then the training process is simple, but the model performance is limited due to inability to focus on challenging samples
Solution Approach 1:
The patent introduces local quality by assigning different weights to different sets of negative samples. The first set of negative samples (easier) receives a first weight while the second set (harder) receives a second weight, allowing the training process to focus computational resources on difficult samples. This weighted approach balances training process complexity with improved model performance.
Solution Approach 2:
The patent applies dynamics by making the training process adaptive through weight adjustment. Instead of static equal weighting, the method dynamically assigns different weights to different negative sample sets based on their difficulty, allowing the training process to adapt to the varying characteristics of samples and improve overall model performance.
Data Source
AI summary
A method, apparatus, device, and medium for managing a model based on a distance between samples. In one method, a basic sample for training a contrastive learning model and a plurality of negative samples associated with the basic sample is obtained; a sequence of the plurality of negative samples is generated based on distances between the plurality of negative samples and the basic sample; the sequence of the plurality of negative samples is divided into a first set of negative samples and a second set of negative samples; an update parameter for updating the contrastive learning model is determined based on the basic sample, the first set of negative samples and a first weight of the first set of negative samples, and the second set of negative samples and a second weight of the second set of negative samples, the first weight is greater than the second weight.