What is learning without Forgetting?
When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities.
What is meant by transfer learning?
Transfer learning is the application of knowledge gained from completing one task to help solve a different, but related, problem. Through transfer learning, methods are developed to transfer knowledge from one or more of these source tasks to improve learning in a related target task.
When would you not use transfer learning?
The problem of negative transfer Transfer learning only works if the initial and target problems of both models are similar enough. If the first round of training data required for the new task is too far from the data of the old task, then the trained models might perform worse than expected.
What is incremental training?
In computer science, incremental learning is a method of machine learning in which input data is continuously used to extend the existing model’s knowledge i.e. to further train the model. Algorithms that can facilitate incremental learning are known as incremental machine learning algorithms.
What are progressive neural networks?
A progressive neural network is a technique in the eld of transfer learning, to solve the problem of catastrophic forgetting. Catastrophic forgetting is an area of machine learning, in which machines forget the previously known data while training a new model in transfer learning.
When should we use transfer learning?
When to use Transfer Learning There already exists a network that is pre-trained on a similar task, which is usually trained on massive amounts of data. When task 1 and task 2 have the same input.