Towards AI that is forever learning efficiently

该项目属于EPSRC统计和应用概率研究领域。它将着眼于人工智能的研究技术，这些技术允许模型在不同的数据集上不断学习，包括适应在当前训练数据上的表现，以及不“忘记”如何在以前的数据上表现得很好。如果神经网络训练到某个时间点的所有数据都不是独立相同分布的，我们可能会遇到问题，因为用于训练网络的梯度更新平均而言并没有改变神经网络，使其对所有数据执行最优。这个问题出现在许多领域，包括当我们试图针对包含不同数据的任务序列训练网络时，在这种情况下，网络将优化当前数据和先前数据的松散性能，这被称为灾难性遗忘。名为“持续学习”的研究领域着眼于如何在这种情况下训练模型，该研究领域需要克服的重要问题之一是减轻灾难性遗忘。在这一领域的研究可能导致能够在部署模型的同时使用新数据不断更新模型，能够通过与模型交互来教授模型，并更容易地在非常大的数据集上进行培训。它还可能着眼于允许有效利用数据的研究技术。一种更有效地使用数据的方法是能够处理更多的数据，即。要求较低的数据。一个典型的例子是处理未标记的数据，以便能够在图像分类任务中获得良好的性能，而我们只有少量的标记图像可用。这类技术擅长学习表示数据的方法，这些数据可能对无数下游应用程序有用。专注于尝试训练模型的领域称为表示学习，这些模型擅长以一种对下游应用程序有用的非监督方式从数据中提取信息。(无监督的)表示模型的优点在于，它们允许在更大量的数据上训练模型，从而解决了由于数据限制而以前无法解决的问题。该项目也可能着眼于这两个研究领域的交集，即。不断从要求较低的数据中学习，例如不需要标签。这是一个研究人员尚未广泛探索的领域，可能会对我们可以应用深度学习解决的问题产生很大影响。它可以允许模型在部署时可以根据原始传入数据不断进行训练，永远改进和学习，而不需要漫长的数据准备。这个项目的目的和目标包括：开发新的方法来促进在持续环境中的学习。开发有效利用数据进行表征学习的新方法，包括对要求较低的数据进行表征学习(例如，没有标签的图像)。开发新的方法，在持续的环境中促进学习，并有效地使用数据。将这些方法应用于问题，以经验性地验证其性能。

This project falls within the EPSRC Statistics and Applied Probability research area. It will look at researching techniques for AI that allow a model to continually learn on different datasets which includes adapting to perform on current training data and not 'forgetting' how to perform well on previous data. If all the data a neural network has trained on up to a certain point in time is not independently identically distributed, we can run into a problem as the gradient updates used to train the network aren't on average changing the neural network to perform optimally on all the data. This problem arises in many areas including when we try to train a network on a sequence of tasks containing different data, in this scenario the network will optimize on the current data and loose performance on previous data which has been named catastrophic forgetting. The area of research named 'continual learning' looks at how to train models in such a scenario and one of the significant issues to overcome for this research area is mitigating catastrophic forgetting. Research in this area could lead to being able to continually update models with new data whilst its deployed, being able to teach models by interacting with them and more easily train on very large datasets. It may also look at researching techniques that allow for the efficient use of data. One way to more efficiently use data is being able to work with more data, ie. data with less requirements. A classic example of this is working with unlabelled data to be able to get good performance on an image classification task where we only have a small number of labelled images available. Such techniques are good at learning ways to represent data that can be useful for a myriad of downstream applications. The area that focuses on trying to train models that are good at extracting information from data in an unsupervised way that is useful for downstream applications is called representation learning. Advantages of (unsupervised) representation models are that they allow for training models on much larger amounts of data and therefore solve problems that due to data restrictions were previously unsolvable. The project may also look at the intersection of these two research areas, ie. continual learning from data with less requirements such as without the need for labels. This is an area not yet widely explored by researchers and could have a large impact on the kind of problems we can apply deep learning to. It could allow for models that whilst deployed can train continually on raw incoming data, forever improving and learning and without the need for lengthy data preparation. Aims and objectives of this project include: Develop new methods to facilitate learning in the continual setting. Develop new methods for the efficient use of data for representation learning which includes representation learning on data with less requirements (eg. images without labels). Develop new methods that facilitate learning in the continual setting that are efficient at using data. Apply these methods to problems to validate their performance empirically.