Parallel Double-Batched Self-Distillation for Resource-Constrained Image Recognition
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Solution Overview
Problem
Deep neural networks (DNNs) face challenges in resource-constrained devices due to high computational and memory costs, and existing acceleration methods like network pruning and low-rank factorization are cumbersome, introduce bias, or require extensive retraining, while knowledge distillation techniques fail to assimilate knowledge between original and transformed data.
Innovation Solution
The introduction of parallel double-batched self-distillation (PadBas), which involves data augmentation, parameter sharing between teacher and student networks, and knowledge alignment to maintain accuracy and efficiency, allowing for compact and expressive network structures that can be trained on resource-constrained devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If knowledge distillation is used to compress DNN models, then computational and memory costs are reduced, but knowledge between original and transformed data is not assimilated
Solution Approach 1:
The training process is segmented into two distinct branches: a first branch for original data and a second branch for transformed data. Each branch maintains separate student and teacher networks that process different data subsets, allowing parallel knowledge distillation while preserving both original and transformed knowledge representations.
Solution Approach 2:
The patent introduces a dual-branch dimensional structure where the first branch handles original data and the second branch handles transformed data. This dimensional separation allows the system to simultaneously process and distill knowledge from both data types without interference, resolving the information loss problem while maintaining computational efficiency.
2Productivity
If network pruning or low-rank factorization is used to accelerate DNN execution, then computational efficiency is improved, but the methods are cumbersome and introduce bias
Solution Approach 1:
The system performs self-distillation where student networks in both branches distill knowledge from their respective teacher networks using the same data augmentation pipeline. This self-service approach eliminates the need for complex external pruning or factorization methods, achieving acceleration through intrinsic knowledge distillation mechanisms that are simpler and less biased.
Solution Approach 2:
The patent changes the training parameters by introducing dual branches with separate student-teacher pairs that share parameters across branches. This parameter sharing mechanism simplifies the overall system compared to traditional pruning methods while maintaining computational efficiency through coordinated parameter updates across both branches.
3Measurement precision
If deeper and wider networks are used to improve accuracy, then model performance increases, but resource consumption increases
Solution Approach 1:
The patent merges the first branch (original data) and second branch (transformed data) through parameter sharing between student networks. This merging allows the system to achieve high accuracy through combined knowledge from both data types while using shared parameters to reduce overall resource consumption compared to training separate independent models.
Solution Approach 2:
The student networks in both branches serve multiple functions: they process original data, process transformed data, and distill knowledge from teacher networks. This multi-functionality allows the system to achieve high accuracy through versatile network usage while avoiding redundant resource allocation, effectively addressing the accuracy-resource tradeoff.
Data Source
AI summary
Methods and apparatus to perform parallel double-batched self-distillation in resource-constrained image recognition environments are disclosed herein. Example apparatus disclosed herein are to identify a source data batch and an augmented data batch, the augmented data generated based on at least one data augmentation technique. Disclosed example apparatus is also to share one or more parameters between a student neural network corresponding to the source data batch and a teacher neural network corresponding to the augmented data batch, the one or more parameters including one or more convolution layers to be shared between the teacher neural network and the student neural network. Disclosed example apparatus is further to align knowledge corresponding to the teacher neural network and the student neural network, the knowledge corresponding to the one or more parameters shared between the student neural network and the teacher neural network.