Table of Contents
How do you overcome catastrophic forgetting in neural networks?
Robins (1995) described that catastrophic forgetting can be prevented by rehearsal mechanisms. This means that when new information is added, the neural network is retrained on some of the previously learned information. In general, however, previously learned information may not be available for such retraining.
Why is catastrophic forgetting bad?
However, standard neural network architectures suffer from catastrophic forgetting which makes it difficult for them to learn a sequence of tasks. Without solving this problem, an NN is hard to adapt to lifelong or continual learning, which is important for AI.
Does continual learning catastrophic forgetting?
Continual learning algorithms try to achieve this same ability for the neural networks and to solve the catastrophic forgetting problem. Thus, in essence, continual learning performs incremental learning of new tasks.
Do Neural Networks forget?
In learning how to do each new task, humans don’t forget previous ones. Artificial neural networks, on the other hand, struggle to learn continually and consequently suffer from catastrophic forgetting: the tendency to lose almost all information about a previously learned task when attempting to learn a new one.
What is distillation in deep learning?
In machine learning, knowledge distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized.
What is experience replay in reinforcement learning?
Experience Replay is a replay memory technique used in reinforcement learning where we store the agent’s experiences at each time-step, e t = ( s t , a t , r t , s t + 1 ) in a data-set D = e 1 , ⋯ , e N , pooled over many episodes into a replay memory.
Does an Lstm forget more than a CNN an empirical study of catastrophic forgetting in NLP?
Our primary finding is that CNNs forget less than LSTMs. We show that max-pooling is the underlying operation which helps CNNs alleviate forgetting compared to LSTMs.
What is inference in machine learning?
Machine learning (ML) inference is the process of running live data points into a machine learning algorithm (or “ML model”) to calculate an output such as a single numerical score. ML inference is the second phase, in which the model is put into action on live data to produce actionable output.
What is distilled model?
Knowledge Distillation is a procedure for model compression, in which a small (student) model is trained to match a large pre-trained (teacher) model. Knowledge is transferred from the teacher model to the student by minimizing a loss function, aimed at matching softened teacher logits as well as ground-truth labels.
What is model compression?
Model compression is the technique of deploying state-of-the-art deep networks in devices with low power and resources without compromising on the model’s accuracy. Compressing or reducing in size and/or latency means the model has fewer and smaller parameters and requires lesser RAM.
What is catastrophic forgetting in machine learning?
Shortly, catastrophic forgetting is the radical performance drops of the model f ( X; θ) f ( X; θ) which parameterized by θ θ with input X X — mostly neural networks exhibit distributed representation [1] — that map X → Y X → Y performing on previously learned tasks t t t t after learning on task t n t n where t < n. Figure 1.
Is relearning an essential measure in catastrophic forgetting?
Frequently overlooked by existing recent experiments, relearning is another essential measure in catastrophic forgetting which was initially proposed in physiological study by Hermann Ebbinghaus known as ‘savings’ but implemented as metrics in catastrophic forgetting by Hetherington [13].
What is the best way to measure forgetting?
For forgetting measurement, the main approach is to revisit a task after training on later tasks and compare the accuracy before and after².