E-Stitchup: Data Augmentation for Pre-Trained Embeddings
This work addresses the challenge of enhancing model reliability and efficiency in production-level deep learning systems, though it is incremental as it builds on existing Mixup techniques.
The paper tackles the problem of improving classification performance and calibration for models using pre-trained embeddings by proposing data augmentation methods that combine embeddings with label softening, resulting in increased accuracy, reduced training time, and better confidence calibration.
In this work, we propose data augmentation methods for embeddings from pre-trained deep learning models that take a weighted combination of a pair of input embeddings, as inspired by Mixup, and combine such augmentation with extra label softening. These methods are shown to significantly increase classification accuracy, reduce training time, and improve confidence calibration of a downstream model that is trained with them. As a result of such improved confidence calibration, the model output can be more intuitively interpreted and used to accurately identify out-of-distribution data by applying an appropriate confidence threshold to model predictions. The identified out-of-distribution data can then be prioritized for labeling, thus focusing labeling effort on data that is more likely to boost model performance. These findings, we believe, lay a solid foundation for improving the classification performance and calibration of models that use pre-trained embeddings as input and provide several benefits that prove extremely useful in a production-level deep learning system.