Autonomous Cross Domain Adaptation Under Extreme Label Scarcity.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Data Science

Background:

• Cross-domain multistream classification presents challenges due to the need for rapid domain adaptation in dynamic environments.
• Existing methods often require fully labeled source stream data, leading to high labeling costs.
• A significant problem is extreme label shortage, where only minimal labeled source data is available.

Purpose of the Study:

• To develop a novel approach for cross-domain multistream classification under conditions of extreme label shortage.
• To introduce a method that minimizes the need for labeled source stream data.
• To enhance classification accuracy and efficiency in rapidly changing, multi-stream environments.

Main Methods:

• The proposed solution, LEOPARD (Learning Streaming Process from Partial Ground Truth), utilizes a flexible deep clustering network.
• The network dynamically adjusts its structure (nodes, layers, clusters) based on data distribution shifts.
• Key techniques include simultaneous feature learning and clustering for latent space optimization and adversarial domain adaptation for domain invariance.

Main Results:

• LEOPARD demonstrated improved performance in 15 out of 24 evaluated cases compared to prominent algorithms.
• The deep clustering strategy facilitates the creation of clustering-friendly latent spaces.
• Adversarial domain adaptation effectively trains feature extractors to be domain-invariant.

Conclusions:

• LEOPARD offers an effective solution for cross-domain multistream classification with extreme label shortage.
• The dynamic deep clustering and adversarial domain adaptation strategies contribute to its robust performance.
• The method shows promise for real-world applications requiring efficient adaptation to changing data streams.