Neural Network Knowledge Distillation via Latent Target Data
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Solution Overview
Problem
Existing knowledge distillation methods for neural networks require significant computational resources and often result in trivial solutions due to the one-to-one relationship between teacher and student models, limiting their efficiency in transferring knowledge effectively.
Innovation Solution
The method involves predicting first and second latent target data using a connectionist temporal classification (CTC) algorithm and cross entropy loss functions, establishing a many-to-one relationship between the latent and target data to train the neural network model, allowing for efficient knowledge distillation with shared parameters between teacher and student models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing knowledge distillation methods are used with one-to-one relationship between teacher and student models, then knowledge transfer can be performed, but computational resources are significantly required and trivial solutions occur
Solution Approach 1:
The patent segments the knowledge distillation process into two distinct training phases: (1) teacher model training with masked target data to generate latent target data, and (2) student model training using the generated latent target data. This segmentation allows independent optimization of each model without requiring simultaneous computation, reducing overall computational resource requirements while maintaining effective knowledge transfer.
Solution Approach 2:
The patent applies preliminary action by first training the teacher model to generate latent target data before training the student model. The teacher model processes source data and generates latent target data that encapsulates knowledge patterns, which are then used as training targets for the student model. This preliminary generation of training data reduces the computational burden during student model training compared to traditional simultaneous training approaches.
2Ease of manufacture
If existing knowledge distillation methods are used with one-to-one relationship, then model training can be performed, but trivial solutions occur due to direct mapping constraints
Solution Approach 1:
The patent introduces latent target data as an intermediary between the teacher model and student model. Instead of direct one-to-one mapping, the teacher model generates latent target data that serves as an intermediate representation containing distilled knowledge. This intermediary layer prevents trivial solutions by requiring the student model to learn from the transformed latent representations rather than directly copying teacher outputs, thereby improving solution quality while maintaining training feasibility.
3Use of energy by moving object
If many-to-one relationship is established between latent target data and target data, then computational requirements are reduced, but training complexity increases
Solution Approach 1:
The patent applies parameter changes by transforming the traditional one-to-one mapping parameters into a many-to-one relationship where multiple source data instances map to a single latent target data representation. This parameter transformation reduces computational requirements by consolidating information into latent representations. The training complexity is managed through structured loss functions that guide the many-to-one mapping process, balancing computational efficiency with training complexity.
Data Source
AI summary
Provided is a method and an apparatus with neural network (NN) training. A method of operating a neural network model includes predicting first latent target data based on source data and based on target data corresponding to the source data, predicting second latent target data based on the source data and based on constant data, and training the NN model based on the first latent target data and the predicted second latent target data; the first latent target data and the target data have a many-to-one relationship.