Collaborating Authors

Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics Machine Learning

A central challenge in developing versatile machine learning systems is catastrophic forgetting: a model trained on tasks in sequence will suffer significant performance drops on earlier tasks. Despite the ubiquity of catastrophic forgetting, there is limited understanding of the underlying process and its causes. In this paper, we address this important knowledge gap, investigating how forgetting affects representations in neural network models. Through representational analysis techniques, we find that deeper layers are disproportionately the source of forgetting. Supporting this, a study of methods to mitigate forgetting illustrates that they act to stabilize deeper layers. These insights enable the development of an analytic argument and empirical picture relating the degree of forgetting to representational similarity between tasks. Consistent with this picture, we observe maximal forgetting occurs for task sequences with intermediate similarity. We perform empirical studies on the standard split CIFAR-10 setup and also introduce a novel CIFAR-100 based task approximating realistic input distribution shift.

XMixup: Efficient Transfer Learning with Auxiliary Samples by Cross-domain Mixup Machine Learning

Transferring knowledge from large source datasets is an effective way to fine-tune the deep neural networks of the target task with a small sample size. A great number of algorithms have been proposed to facilitate deep transfer learning, and these techniques could be generally categorized into two groups - Regularized Learning of the target task using models that have been pre-trained from source datasets, and Multitask Learning with both source and target datasets to train a shared backbone neural network. In this work, we aim to improve the multitask paradigm for deep transfer learning via Cross-domain Mixup (XMixup). While the existing multitask learning algorithms need to run backpropagation over both the source and target datasets and usually consume a higher gradient complexity, XMixup transfers the knowledge from source to target tasks more efficiently: for every class of the target task, XMixup selects the auxiliary samples from the source dataset and augments training samples via the simple mixup strategy. We evaluate XMixup over six real world transfer learning datasets. Experiment results show that XMixup improves the accuracy by 1.9% on average. Compared with other state-of-the-art transfer learning approaches, XMixup costs much less training time while still obtains higher accuracy.

Combining Ensembles and Data Augmentation can Harm your Calibration Machine Learning

Ensemble methods which average over multiple neural network predictions are a simple approach to improve a model's calibration and robustness. Similarly, data augmentation techniques, which encode prior information in the form of invariant feature transformations, are effective for improving calibration and robustness. In this paper, we show a surprising pathology: combining ensembles and data augmentation can harm model calibration. This leads to a trade-off in practice, whereby improved accuracy by combining the two techniques comes at the expense of calibration. On the other hand, selecting only one of the techniques ensures good uncertainty estimates at the expense of accuracy. We investigate this pathology and identify a compounding under-confidence among methods which marginalize over sets of weights and data augmentation techniques which soften labels. Finally, we propose a simple correction, achieving the best of both worlds with significant accuracy and calibration gains over using only ensembles or data augmentation individually. Applying the correction produces new state-of-the art in uncertainty calibration across CIFAR-10, CIFAR-100, and ImageNet.

Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network Augmentation Machine Learning

Lifelong learning aims to develop machine learning systems that can learn new tasks while preserving the performance on previous tasks. This approach can be applied, for example, to prevent accident on autonomous vehicles by applying the knowledge learned on previous situations. In this paper we present a method to overcomes catastrophic forgetting that learns new tasks and preserves the performance on old tasks without accessing the data of the original model, by selective network augmentation, using convolutional neural networks for image classification. The experiment results showed that our method, in some scenarios outperforms the state-of-art Learning without Forgetting algorithm. Results also showed that in some situations is better to use our model instead of training a neural network using isolated learning.

Data Augmentation for Deep Transfer Learning Machine Learning

Current approaches to deep learning are beginning to rely heavily on transfer learning as an effective method for reducing overfitting, improving model performance, and quickly learning new tasks. Similarly, such pre-trained models are often used to create embedding representations for various types of data, such as text and images, which can then be fed as input into separate, downstream models. However, in cases where such transfer learning models perform poorly (i.e., for data outside of the training distribution), one must resort to fine-tuning such models, or even retraining them completely. Currently, no form of data augmentation has been proposed that can be applied directly to embedding inputs to improve downstream model performance. In this work, we introduce four new types of data augmentation that are generally applicable to embedding inputs, thus making them useful in both Natural Language Processing (NLP) and Computer Vision (CV) applications. For models trained on downstream tasks with such embedding inputs, these augmentation methods are shown to improve the AUC score of the models from a score of 0.9582 to 0.9812 and significantly increase the model's ability to identify classes of data that are not seen during training.