Dynamic Margin Loss for Incremental Learning of Multimedia Recognition
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Solution Overview
Problem
Existing multimedia recognition models face challenges in training effectively with long-tailed data distributions, leading to poor performance in clusters with fewer samples and risk of over-fitting when attempting to balance data.
Innovation Solution
The method involves performing clustering on multimedia features, calculating sub-clusters, and applying balanced sampling to generate balanced samples. A dynamic margin loss function is used in incremental learning, adjusted based on the number of samples in each cluster and sub-cluster to optimize feature space distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If over-sampling or under-sampling is used to re-balance the clusters, then data distribution is improved, but over-fitting occurs
Solution Approach 1:
The patent changes the parameter of margin in the loss function from static to dynamic, adjusting it according to the number of samples in each cluster. This allows the model to adapt to long-tailed data distribution without requiring over-sampling or under-sampling, thereby avoiding over-fitting while improving data distribution balance.
2Measurement precision
If manual labeling is used for training, then recognition performance is improved, but data collection efficiency deteriorates
Solution Approach 1:
The patent enables the system to automatically handle long-tailed data distribution through dynamic margin adjustment and balanced sampling, eliminating the need for manual intervention in data labeling and distribution balancing. The system serves itself by adapting to data distribution issues without human intervention.
3Manufacturing precision
If clusters with fewer samples are trained with more emphasis, then training performance is improved, but over-fitting increases
Solution Approach 1:
The patent introduces dynamic margin adjustment that adapts during training based on the actual number of samples in each cluster. This dynamic approach allows the model to automatically adjust the emphasis given to different clusters, improving training performance for under-represented clusters without manually forcing over-emphasis that would cause over-fitting.
Data Source
AI summary
A method and a system for incremental learning of a multimedia recognition model are provided, which merge multimedia samples collected by a semi-supervised algorithm with the present dataset by using a two-stage clustering method. The multimedia recognition model is optimized by a dynamic margin that is finely adjusted in balanced sampling performed on clusters and sub-clusters.