E-Stitchup: Data Augmentation for Pre-Trained Embeddings (1912.00772v2)

Abstract: 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.

• The paper introduces E-Stitchup, a novel data augmentation technique that selects embedding values at random indices to boost calibration and classification performance.
• It leverages embedding-based methods inspired by Mixup, including E-Mixup and its softened variants, to mitigate overconfidence from one-hot labels.
• Experiments on Fashion Product Images show faster convergence and improved AUROC and AUPR metrics, offering practical gains in model efficiency and robustness.

E-Stitchup: Data Augmentation for Pre-Trained Embeddings

The paper "E-Stitchup: Data Augmentation for Pre-Trained Embeddings" presents a methodology designed to enhance the performance of downstream deep learning models using embeddings from pre-trained models as inputs. The authors introduce data augmentation techniques, focusing on the improvement of model calibration, classification accuracy, and training efficiency.

Core Methodologies

The primary concept revolves around embedding-based data augmentation inspired by Mixup. The authors propose two main augmentation strategies: E-Mixup and E-Stitchup.

1. E-Mixup: This method involves creating a weighted average of embeddings and their associated target vectors. The weighting is randomized by sampling lambda from a Beta distribution. E-Mixup effectively eliminates one-hot labels by averaging target vectors, aiming to mitigate overconfidence and improve calibration.
2. E-Stitchup: Rather than interpolating, E-Stitchup selects values at random indices from two embeddings, directed by the lambda parameter. This approach preserves the integrity of embedding values more robustly than standard Mixup techniques, showing superior performance in both calibration and accuracy metrics.

Additionally, the authors explore variations combining the embedding techniques with label softening to further smooth out label distributions, known as Soft E-Mixup and Soft E-Stitchup.

Experimental Evaluation

The investigation is executed using the Fashion Product Images dataset, employing pre-trained models like BERT for text embeddings and EfficientNet for image embeddings. The downstream classification model is a lightweight fully-connected neural network prioritized for its adaptability and conjunctional training with embedding inputs.

The experiments reveal that models utilizing embedding augmentation converge more rapidly and exhibit better calibrated outputs compared to control models. E-Stitchup, in particular, demonstrates improved AUROC and AUPR metrics, indicating enhanced classification performance and confidence calibration. Soft variants of the methods generally provide superior classification results, albeit with marginally less calibration than their non-softened counterparts.

Discussion

The paper emphasizes the significance of confidence calibration, indicating that the control models suffer from overconfidence—a pervasive issue in modern models trained with one-hot labels. The novel embedding augmentations mitigate this by distributing confidence levels more effectively across predictions.

Embedding augmentations like E-Stitchup not only enhance model generalization and calibration without any modifications to the pre-trained models but also offer an actionable procedure for efficiently identifying and incorporating new classes of data—an essential feature for dynamic, real-world applications where data evolves over time.

Implications and Future Work

In practical terms, the proposed embedding augmentation methods have substantial implications in production environments, providing a framework for reduced computational load and improved model interpretability. They offer particular advantages in active learning scenarios, allowing systems to intelligently focus labeling efforts and optimize model performance incrementally.

Future explorations could involve extending these augmentation techniques to other machine learning paradigms, investigating additional pre-trained models, or enhancing related strategies for handling new data classes in evolving datasets. The broader applicability of this framework across different modalities, such as speech or video data, remains an intriguing avenue for continued research.

In conclusion, the paper presents a concise, effective strategy for leveraging pre-trained embeddings, offering notable advancements in model robustness and efficiency without the need for extensive alterations to existing architectures. E-Stitchup, in particular, stands out as an optimal approach for both improved calibration and classification performance, laying groundwork for robust, nuanced deep learning systems.