Stochastic Quantization for Auto-Encoder Parameter Convergence
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Solution Overview
Problem
In auto-encoders, the loss function cannot be differentiated with respect to quantized image feature amounts, leading to suboptimal parameter convergence and deteriorated image reconstruction quality due to the assumption of a differential value of 1 for quantized image feature amounts.
Innovation Solution
An information processing system that includes distribution estimation and sampling devices using machine learning models to determine and sample probability distributions within a predetermined value range, allowing for the generation of sample values and code sequences through entropy encoding and decoding, thereby optimizing model parameters under a combined loss function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If quantized image feature amounts are used in the loss function, then the parameter set can be updated using differential values, but the loss function cannot be differentiated with respect to quantized values leading to suboptimal parameter convergence
Solution Approach 1:
The patent introduces a stochastic quantization mechanism as an intermediary between continuous feature amounts and discrete code sequences. By treating quantization as a probabilistic process rather than a deterministic operation, the system enables gradient computation through the expectation of quantized values, allowing the loss function to be differentiated while still utilizing quantized representations for compression.
Solution Approach 2:
The patent changes the parameter representation from fixed quantized values to probability distributions over quantized values. This parameter transformation allows the system to work with continuous probability distributions during training (enabling differentiation) while still producing discrete code sequences during inference (maintaining compression efficiency).
2Reliability
If the differential value of quantized image feature amounts is assumed to be 1, then the loss function can be computed, but the parameter set does not converge to an optimum solution causing deterioration in reconstructed image quality
Solution Approach 1:
The patent implements a feedback mechanism where the quantization distribution parameters are learned and updated based on the loss function gradients. The system continuously adjusts the quantization strategy based on reconstruction error feedback, allowing the model to optimize both compression efficiency and reconstruction quality simultaneously through iterative training.
Solution Approach 2:
The patent transforms the static quantization process into a dynamic learned process. Instead of using fixed quantization thresholds, the system learns optimal quantization parameters through training, allowing the quantization behavior to adapt dynamically to the specific characteristics of the input data and optimization goals.
Data Source
AI summary
A first distribution estimating device determines a first probability distribution of quantized values in a predetermined value range corresponding to an input value, by using a first machine learning model. A first sampling device samples the quantized values and determines a first sample value, using the first probability distribution. A second distribution estimating device determines a second probability distribution corresponding to the first sample value, by using a second machine learning model. A second sampling device samples the quantized values in the value range and determines a second sample value, using the second probability distribution. It can be implemented in the form of any of an information processing system, an encoding device, a decoding device, a model learning device, an information processing method, an encoding method, a decoding method, a model learning method, and a program storage medium.