Domain-Agnostic Feature Mapping for CNN Adaptation
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Solution Overview
Problem
Deep convolutional neural networks require extensive annotated training data, which is time-consuming and costly to obtain, limiting their scalability in adapting to new image domains without additional labels.
Innovation Solution
A system that adapts a deep CNN by determining domain-agnostic features mapping from annotated source to target domains through a joint latent space, using auxiliary networks and loss functions, and adversarial settings to reconstruct images and classify domain agnosticism, enabling label prediction in target domains without new annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep CNN is trained on annotated source domain, then classification performance is improved, but the system cannot be applied to target domain without new annotations
Solution Approach 1:
The system segments the feature extraction process into domain-specific and domain-agnostic components. The CNN extracts features that are separated into domain-specific characteristics (useful for classification) and domain-agnostic characteristics (useful for transfer). This allows the model to maintain high classification performance on the source domain while extracting transferable features for the target domain.
Solution Approach 2:
The system introduces an intermediary domain-agnostic feature space that mediates between the source domain annotated data and the target domain unannotated data. This intermediate representation allows knowledge transfer without requiring direct mapping between domains, enabling adaptation to new domains while preserving source domain performance.
2Measurement precision
If human annotations are obtained for training, then training accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The system creates a copy of the annotation knowledge from the source domain and transfers it to the target domain through domain-agnostic feature extraction. Instead of obtaining new annotations for the target domain, the system copies the learned classification knowledge and adapts it to the new domain, significantly reducing annotation time and cost while maintaining training accuracy.
3Measurement precision
If domain-specific features are extracted, then source domain classification is improved, but transfer to target domain becomes difficult
Solution Approach 1:
The system applies local quality by making different parts of the feature extraction process serve different purposes. The CNN extracts both domain-specific features (with local characteristics useful for source domain classification) and domain-agnostic features (with universal characteristics useful for transfer). This dual-nature feature extraction allows simultaneous optimization for source domain performance and target domain adaptability.
Data Source
AI summary
Described is a system for adapting a deep convolutional neural network (CNN). A deep CNN is first trained on an annotated source image domain. The deep CNN is adapted to a new target image domain without requiring new annotations by determining domain agnostic features that map from the annotated source image domain and a target image domain to a joint latent space, and using the domain agnostic features to map the joint latent space to annotations for the target image domain.