Cross-Domain Computer Vision Transfer Learning with Normalized Backbones
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Solution Overview
Problem
Existing transfer learning techniques are ill-suited for scenarios where significant differences exist between the training data for the initial and downstream computer vision tasks, particularly when the tasks operate in different domains, such as natural images versus medical images, leading to poor performance and inefficiencies in sample efficiency and hyperparameter tuning.
Innovation Solution
Implementing group normalization and weight standardization in the backbone layers shared between initial and downstream neural networks, along with adjusting hyperparameters based on the size and characteristics of the downstream training data, to enhance the transfer of representations learned during pre-training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional transfer learning techniques are used to train neural networks for downstream tasks from different domains, then the training process can be simplified, but the performance on downstream tasks deteriorates due to significant differences between initial and downstream training data
Solution Approach 1:
The patent applies weight standardization and group normalization techniques to modify the parameter distribution in shared backbone layers. This allows the model to adapt to domain shifts between initial and downstream tasks while maintaining the benefits of transfer learning, thereby improving downstream task performance without sacrificing training efficiency
Solution Approach 2:
The neural network is segmented into shared backbone layers and task-specific layers. The shared backbone layers undergo weight standardization and group normalization to handle domain differences, while task-specific layers are trained separately. This segmentation allows different parts of the network to be optimized for their specific functions, resolving the contradiction between training ease and performance
2Quantity of substance
If transfer learning is applied across different domains (e.g., natural images to medical images), then sample efficiency can be improved, but the representation transfer effectiveness deteriorates due to domain differences
Solution Approach 1:
Weight standardization and group normalization are applied to transform the parameter distributions in shared layers to be more compatible across domains. This parameter transformation preserves useful representations from the initial task while adapting them to the downstream domain, thereby maintaining representation transfer effectiveness even when transferring across significantly different domains with limited downstream samples
Solution Approach 2:
The shared backbone layers act as an intermediary between the initial task and downstream task. By applying weight standardization and group normalization to these intermediary layers, the system facilitates effective representation transfer across domain boundaries, preserving information while adapting to new domains
3Device complexity
If fixed hyperparameters are used for fine-tuning, then the training process is simplified, but the performance deteriorates when downstream training data size varies
Solution Approach 1:
The patent implements dynamic hyperparameter adjustment during fine-tuning based on the size of the downstream training dataset. Instead of using fixed hyperparameters, the system adapts learning rates and other hyperparameters according to the available data, which improves downstream task performance across varying data sizes while maintaining reasonable training complexity through automated adjustment
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to perform a downstream computer vision task. One of the methods includes pre-training an initial neural network that shares layers with the neural network to perform an initial computer vision task and then training the neural network on the downstream computer vision task.