Calibrated Distillation With Two-Stage Loss for Fast Convergence
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Solution Overview
Problem
Existing knowledge distillation methods fail to achieve both high-quality and fast convergence in training student models due to the inability to minimize loss at the probability mean value of teacher predictions while maintaining a convex loss surface in logit and probability domains.
Innovation Solution
A two-stage distillation approach is employed, using a first loss function for fast convergence in the logit space followed by a second loss function for calibration in the probability space to ensure convergence to the correct optimum.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a single loss function is used for distillation training, then the training process is simple, but the model fails to converge to the correct optimum in probability space
Solution Approach 1:
The distillation training process is segmented into two distinct stages: (1) logit space matching using a first loss function to achieve fast convergence, and (2) probability space calibration using a second loss function to ensure convergence to the correct optimum. This segmentation allows each stage to optimize for its specific objective without interference from the other.
Solution Approach 2:
The patent changes the parameter space and loss function type between training stages. In the first stage, optimization occurs in logit space with a loss function designed for fast convergence. In the second stage, the parameter space transitions to probability space with a different loss function that ensures calibration accuracy, effectively changing the optimization landscape to achieve the desired convergence properties.
2Productivity
If fast convergence is prioritized in logit space, then training speed improves, but probability calibration accuracy deteriorates
Solution Approach 1:
The training process is divided into two sequential segments: first, logit space matching that prioritizes fast convergence and training speed; second, probability space calibration that prioritizes accuracy. This segmentation allows the system to achieve fast convergence initially, then refine the probability calibration in a subsequent stage without compromising the speed benefits.
Solution Approach 2:
The patent performs preliminary action by first achieving fast convergence in logit space before proceeding to probability calibration. The initial fast convergence establishes a good starting point, and the subsequent calibration step refines the probability predictions, ensuring both speed and accuracy are achieved in sequence rather than requiring a compromise in a single stage.
3Measurement precision
If high-quality convergence is achieved through proper calibration, then model accuracy improves, but computational resources increase
Solution Approach 1:
The computational process is segmented into two phases with different resource demands: the first phase (logit space matching) provides fast convergence with moderate computational resources, and the second phase (probability calibration) refines accuracy with additional but targeted computational investment. This segmentation ensures that high model accuracy is achieved while avoiding excessive overall computational resource consumption by optimizing each phase's resource usage.
Data Source
AI summary
Provided are techniques for the calibration of distillation learning from a teacher model to a student model. Specifically, the present disclosure proposes systems and methods that provide convergence with both high quality and speed. That is, example proposed systems both enable the distillation loss to be minimized at the probability mean value in the probability domain of the teacher's predictions distributions while also providing a loss that is nicely (e.g., symmetrically and/or strongly) convex around an optimum in the logit and/or probability domains (e.g., including far from the minimum) to encourage fast convergence of gradient based methods (e.g., irrespective of distance from the minimum).