Sedna v0.3.0 release
Lifelong learning
- Support edge-cloud synergy lifelong learning feature:
- leverages the cloud knowledge base which empowers the scheme with memory ability, which helps to continuously learn and accumulate historical knowledge to overcome the catastrophic forgetting challenge.
- is essentially the combination of another two learning schemes, i.e., multi-task learning and incremental learning, so that it can learn unseen tasks with shared knowledge among various scenarios over time.
- Add lifelong learning example.
Lib refactor
- By using a registration of class-factory functions to emulate virtual constructors, developers can invoke different components by change variables in the Config file.
- Clean up and redesign the base Config class, each feature maintains it's specific variables, and ensures that developers can be manually updated the variables.
- Decouple the ML framework from the features of sedna, allows developers to choose their favorite framework.
- Add a common file operation and a unified log-format in common module, use an abstract base class to standardize the feature modules' interface, and features are invoked by inheriting the base class.
Published images
The published images can be found under docker.io/kubeedge:
kubeedge/sedna-gm:v0.3.0
kubeedge/sedna-lc:v0.3.0
kubeedge/sedna-kb:v0.3.0
kubeedge/sedna-example-joint-inference-helmet-detection-big:v0.3.0
kubeedge/sedna-example-joint-inference-helmet-detection-little:v0.3.0
kubeedge/sedna-example-incremental-learning-helmet-detection:v0.3.0
kubeedge/sedna-example-federated-learning-surface-defect-detection-train:v0.3.0
kubeedge/sedna-example-federated-learning-surface-defect-detection-aggregation:v0.3.0
kubeedge/sedna-example-lifelong-learning-atcii-classifier:v0.3.0