Contrastive Learning Dropout Policy via Reinforcement
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Solution Overview
Problem
Contrastive learning models face performance degradation and instability due to randomly generated positive sample embeddings, which miss important information for classification and are not effectively trained using randomly dropped neurons.
Innovation Solution
A method for contrastive learning that dynamically selects dropout ratios and locations based on reinforcement learning, generating positive sample embeddings using a dropout policy that identifies specific neuron connections to drop, maximizing a loss function through a Monte Carlo search tree and policy network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If random dropout is used to generate positive sample embeddings, then the training process is simple and fast, but the classification accuracy and model performance degrade due to loss of important information
Solution Approach 1:
The patent implements feedback by using reinforcement learning where the loss function provides reward signals to guide the dropout policy selection. The Monte Carlo search tree explores different dropout configurations and uses the loss function feedback to identify which dropout patterns maximize classification performance, thereby resolving the contradiction between simple random dropout and accurate classification.
Solution Approach 2:
The patent makes the dropout policy dynamic by selecting different dropout ratios and locations based on the specific training sample and loss function evaluation. Instead of fixed or random dropout, the system dynamically adapts the dropout configuration to optimize each embedding generation, improving classification accuracy while maintaining training efficiency through targeted exploration.
2Device complexity
If important neuron connections are randomly dropped, then computational complexity is reduced, but the stability and reliability of the contrastive learning model worsen
Solution Approach 1:
The patent changes the parameters of the dropout mechanism by dynamically adjusting dropout ratios and locations based on reinforcement learning feedback. This allows the system to maintain lower computational complexity through selective dropout while improving reliability by optimizing which neurons are dropped to preserve important information for contrastive learning stability.
3Ease of manufacture
If a fixed dropout policy is used, then the training process is simple and consistent, but the adaptability to different training samples and classification tasks is reduced
Solution Approach 1:
The patent applies preliminary action by pre-defining the reinforcement learning framework and Monte Carlo search tree structure before training begins. This setup enables the system to automatically adapt to different training samples during execution, resolving the contradiction between having a simple fixed policy and achieving sample-specific optimization through automated exploration and feedback.
Data Source
AI summary
A method for contrastive learning by selecting dropout ratios and locations based on reinforcement learning includes receiving training data having a positive sample corresponding to a target and negative samples not corresponding to the target. A dropout policy for a neural network is produced based on the training data, where the dropout policy identifies at least one connection between neurons in the neural network to dropout. The training data is encoded, based on the dropout policy, to form embeddings, where the embeddings include multiple positive sample embeddings corresponding to the positive sample and multiple negative sample embedding corresponding to the negative samples.