Prototypical Network Embedding Collapse Prevention
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Solution Overview
Problem
Prototypical networks face challenges in few-shot learning scenarios with large-scale datasets, experiencing embedding collapse where all images are classified as having similar features, limiting their effectiveness in visually classifying classes with limited data.
Innovation Solution
A modified prototypical network algorithm is introduced, incorporating an entropy term and fixed geometry to prevent embedding collapse, and using a fixed anchor point instead of the mean of class points to improve separation and convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If prototypical networks are used for few-shot learning with large-scale datasets, then classification is performed using limited data, but embedding collapse occurs where all images are classified as having similar features
Solution Approach 1:
The patent applies preliminary anti-action by introducing an entropy regularization term in the loss function that actively prevents embedding collapse before it can occur. The entropy term penalizes concentrated embeddings by encouraging uniform distribution, thereby counteracting the collapsing tendency that would otherwise occur during few-shot learning on large-scale datasets
Solution Approach 2:
The patent changes the parameter representation by transitioning from using class mean embeddings to using fixed anchor point embeddings. This parameter change from dynamic mean calculations to fixed anchor points fundamentally alters how embeddings are constructed, preventing collapse by maintaining stable reference points throughout training
2Ease of operation
If the mean of class points is used as the prototype, then the embedding is computed dynamically, but convergence is slow and separation is poor
Solution Approach 1:
The patent applies preliminary action by pre-computing and fixing anchor points for each class before the actual few-shot learning task begins. These fixed anchor points serve as predetermined prototypes that eliminate the need for dynamic mean computation during training, thereby improving convergence speed and classification accuracy while maintaining operational simplicity
Solution Approach 2:
The patent inverts the traditional approach by replacing the dynamic class mean (computed from data) with fixed anchor points (pre-determined references). This inversion transforms the prototype selection from a data-dependent dynamic process to a stable predetermined process, fundamentally improving embedding quality and convergence
Data Source
AI summary
Techniques for training an embedding using a limited training set are described. In some examples, the embedding is trained by generating a plurality of vectors from a random sample of the limited set of training data classes using a layer of the particular machine learning classification model, randomly selecting samples from the plurality of vectors into a set of samples, computing at least one distance for each sampled class from a center parameter for the class using the set of samples, generating a discrete probability distribution over the classes for a query point based on distances to a center parameter for each of the classes in the embedding space, calculating a loss value for the modified prototypical network, the calculation of the loss value being for a fixed geometry of the embedding space and including a measure of the difference between distributions, and back propagating.