Nvidia corporation (20240296205). UNSUPERVISED DOMAIN ADAPTATION WITH NEURAL NETWORKS simplified abstract
Contents
- 1 UNSUPERVISED DOMAIN ADAPTATION WITH NEURAL NETWORKS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 UNSUPERVISED DOMAIN ADAPTATION WITH NEURAL NETWORKS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Unsupervised Domain Transfer Learning
- 1.13 Original Abstract Submitted
UNSUPERVISED DOMAIN ADAPTATION WITH NEURAL NETWORKS
Organization Name
Inventor(s)
David Acuna Marrero of Toronto (CA)
UNSUPERVISED DOMAIN ADAPTATION WITH NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240296205 titled 'UNSUPERVISED DOMAIN ADAPTATION WITH NEURAL NETWORKS
Simplified Explanation
The approaches described in the abstract allow for unsupervised domain transfer learning using neural networks. By training three networks together with labeled data from one domain and unlabeled data from another, the networks can extract features, classify data, and determine the domain of the data.
- Features are extracted using a feature extraction network.
- A first classifier network classifies the data based on these features.
- A second classifier network determines the relevant domain of the data.
- A combined loss function optimizes the networks to extract features for accurate classification while preventing domain determination.
- This optimization enables high accuracy object classification for either domain, even with limited labeled data for the second domain.
Key Features and Innovation
- Unsupervised domain transfer learning using neural networks.
- Training three networks together with labeled and unlabeled data from different domains.
- Feature extraction network, first classifier network, and second classifier network working together.
- Combined loss function for optimizing network performance.
- High accuracy object classification for both domains with limited labeled data.
Potential Applications
- Image classification in different domains.
- Transfer learning in various fields such as healthcare, finance, and manufacturing.
- Improving classification accuracy with limited labeled data.
- Enhancing data analysis and decision-making processes.
Problems Solved
- Overcoming the challenge of domain transfer learning without labeled data.
- Improving object classification accuracy in different domains.
- Enabling efficient knowledge transfer between domains.
Benefits
- Enhanced accuracy in object classification.
- Cost-effective transfer learning without extensive labeled data.
- Improved decision-making based on accurate data analysis.
- Facilitates knowledge transfer between different domains.
Commercial Applications
Unsupervised Domain Transfer Learning for Enhanced Object Classification
This technology can be applied in industries such as e-commerce, healthcare, and security for accurate image classification and data analysis. It can improve product recommendations, medical diagnosis, and security surveillance systems.
Prior Art
Prior research in domain adaptation and transfer learning using neural networks can provide insights into similar approaches and techniques used in related fields.
Frequently Updated Research
Stay updated on advancements in unsupervised domain transfer learning, neural network optimization, and feature extraction techniques to enhance the performance of this technology.
Questions about Unsupervised Domain Transfer Learning
How does unsupervised domain transfer learning differ from supervised domain transfer learning?
Unsupervised domain transfer learning does not require labeled data from the target domain, unlike supervised domain transfer learning, which relies on labeled data for both source and target domains.
What are the key challenges in implementing unsupervised domain transfer learning in real-world applications?
The main challenges include domain shift, feature misalignment, and limited labeled data in the target domain, which can affect the performance of the transfer learning process.
Original Abstract Submitted
approaches presented herein provide for unsupervised domain transfer learning. in particular, three neural networks can be trained together using at least labeled data from a first domain and unlabeled data from a second domain. features of the data are extracted using a feature extraction network. a first classifier network uses these features to classify the data, while a second classifier network uses these features to determine the relevant domain. a combined loss function is used to optimize the networks, with a goal of the feature extraction network extracting features that the first classifier network is able to use to accurately classify the data, but prevent the second classifier from determining the domain for the image. such optimization enables object classification to be performed with high accuracy for either domain, even though there may have been little to no labeled training data for the second domain.
