Dynamic Loss Weighting for Student Model Training
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Solution Overview
Problem
Existing object detection methods using neural networks face issues when the teacher model is not highly accurate, leading to incorrect learning directions and re-learning of incorrect answers, especially when the teacher model is less confident or outputs incorrect results.
Innovation Solution
A learning apparatus that calculates a total loss using weights that increase based on the confidence of the teacher model's output, the difference between the teacher and student model outputs, and the true value, allowing for correct learning even with a low-accuracy teacher model, by incorporating specific loss calculations (L1, L2, L3, and L4) to adjust the training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If knowledge distillation is used to train a student model with a teacher model, then training efficiency is improved and convergence is accelerated, but learning accuracy deteriorates when the teacher model is low accurate
Solution Approach 1:
The patent dynamically changes the weighting parameters of different loss functions based on the teacher model's confidence level and accuracy. When the teacher model is highly confident and accurate, greater weight is given to the distillation loss; when confidence or accuracy is low, the weight shifts toward the ground truth loss, preventing the student model from learning incorrect predictions.
Solution Approach 2:
The patent introduces dynamic adaptive weighting that adjusts the influence of teacher model outputs during training based on real-time confidence scores and accuracy metrics. This dynamic adjustment allows the system to transition between relying on teacher guidance and relying on ground truth, resolving the contradiction between training efficiency and learning accuracy.
2Loss of time
If the student model follows the teacher model's output regardless of accuracy, then training convergence is accelerated, but the student model re-learns incorrect answers
Solution Approach 1:
The patent implements a feedback mechanism where the student model's training process continuously monitors the teacher model's confidence and accuracy. When the teacher model demonstrates low confidence or low accuracy on certain samples, the system provides corrective feedback by increasing the weight of ground truth supervision, preventing the student model from re-learning incorrect answers while maintaining efficient convergence on reliable teacher predictions.
3Device complexity
If equal weight is given to teacher model output and ground truth, then training simplicity is maintained, but learning effectiveness deteriorates when teacher model confidence varies
Solution Approach 1:
The patent changes the parameters (weights) of different loss components based on the teacher model's confidence level. Instead of using fixed equal weights, the system dynamically adjusts the weighting parameters according to the teacher's confidence scores, improving learning effectiveness while maintaining relatively simple training process architecture.
Data Source
AI summary
Teacher and student models output inference results for training data. A loss calculation unit calculates a total loss using at least one of (1) a loss obtained by multiplying a difference between a true value and a student model output by a weight increasing as a confidence of the teacher model output is lower, (2) a loss obtained by multiplying a difference between the true value and the student model output by a weight increasing as a difference between the true value and the teacher model output is greater, and (3) a loss obtained by multiplying a difference between the teacher and student model outputs by weights increasing as the difference between the teacher and student model outputs is greater and increasing as the difference between the true value and the teacher model output is smaller. An update part updates parameters of the student model based on the total loss.