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telecomkh: BrainChip: Key differences between transfer learning and incremental learning

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BrainChip offers insight into two widely accepted forms of deep learning

The massive computing resources required to train neural networks for AI/ML tasks has driven interest in two forms of learning presumed to be more efficient: transfer learning and incremental learning. Experts at BrainChip Holdings Ltd., a leading provider of ultra-low power high performance artificial intelligence technology, offered the following insight and considerations for their use in edge AI/IoT environments.

In transfer learning, applicable knowledge established in a previously trained AI model is “imported” and used as the basis of a new model. After taking this shortcut of using a pretrained model, such as an open-source image or NLP dataset, new objects can be added to customize the result for the particular scenario… READ THE FULL ARTICLE 

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