Embedding Mixup and Stitchup for Deep Learning Generalization
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Solution Overview
Problem
Current deep learning applications face challenges with overfitting and unpredictable performance in real-world environments due to misalignment between training and testing distributions, leading to incorrect predictions and overconfidence issues.
Innovation Solution
The use of embedding mixup, embedding stitchup, softened embedding mixup, and softened embedding stitchup data augmentations to improve the generalization performance of deep learning models by applying weighted averaging and pooling between input embeddings, reducing overfitting and overconfidence, and enabling better identification of out-of-distribution data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If deep learning models are trained on limited data, then training time and computational resources are reduced, but overfitting increases and generalization performance deteriorates
Solution Approach 1:
The patent applies data augmentation techniques (embedding mixup, embedding stitchup) during the training phase to pre-process and expand the training dataset. This preliminary action creates synthetic training samples that diversify the data distribution, enabling the model to learn more robust features from limited original data and improve generalization performance without requiring additional training time
Solution Approach 2:
The patent transforms the training data by applying parameter changes to embedding representations through mixup (linear interpolation with parameter λ) and stitchup (element-wise operations). These parameter transformations create augmented samples that maintain semantic meaning while diversifying the training distribution, allowing better generalization from limited data
2Productivity
If deep learning models use pre-trained embeddings, then training efficiency is improved, but performance on out-of-distribution data deteriorates due to distribution misalignment
Solution Approach 1:
The patent makes the training process dynamic by applying stochastic data augmentation operations during training. The embedding mixup and stitchup operations introduce controlled randomness through parameter sampling (e.g., λ from Beta distribution), enabling the model to adapt to varied data distributions and improve robustness on out-of-distribution data while maintaining training efficiency
Solution Approach 2:
The patent introduces data augmentation operations as intermediary steps between the pre-trained embeddings and the final model training. These intermediary transformations (mixup, stitchup) act as bridges that gradually adapt the model to different data distributions, reducing the shock of distribution misalignment while preserving the benefits of pre-trained embeddings
3Ease of operation
If deep learning models are trained without data augmentation, then training simplicity is maintained, but overconfidence on incorrect predictions increases
Solution Approach 1:
The patent applies partial data augmentation by selectively using embedding mixup and stitchup operations during training. This partial action approach maintains training simplicity for the base model while adding targeted augmentation only where beneficial for confidence calibration, avoiding excessive complexity while still improving prediction reliability on out-of-distribution data
Data Source
AI summary
Systems and methods are provided for generating textual embeddings by tokenizing text data and generating vectors to be provided to a transformer system, where the textual embeddings are vector representations of semantic meanings of text that is part of the text data. The vectors may be averaged for every token of the generated textual embeddings and concatenating average output activations of two layers of the transformer system. Image embeddings may be generated with a convolutional neural network (CNN) from image data, wherein the image embeddings are vector representations of the images that are part of the image data. The textual embeddings and image embeddings may be combined to form combined embeddings to be provided to the transformer system.