Encoder Embedding Robustness Through Adversarial Perturbation Training
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Solution Overview
Problem
Encoder models generate embeddings that are fragile and brittle to small changes in input data, leading to unexpected errors in downstream tasks.
Innovation Solution
Adversarial training data samples are generated to identify perturbations that maximize the distance in the embedding space, and a robust encoder is trained to minimize these differences while maintaining the original embeddings, using a combination of gradient descent and fine-tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the encoder model is trained to generate embeddings that capture relevant information, then the model can effectively represent data samples, but small changes in input data cause outsized effects on embeddings leading to fragile and brittle representations
Solution Approach 1:
The patent applies preliminary action by generating adversarial training data samples before the encoder is fully trained. These adversarial samples are created by deliberately introducing perturbations to training data, allowing the encoder to learn robust representations in advance that can handle small changes in input data without causing outsized effects on embeddings.
Solution Approach 2:
The patent converts the harmful effect of data perturbations into a beneficial training mechanism. By using adversarial samples with intentional perturbations during training, the encoder learns to produce stable embeddings despite input variations. The harmful sensitivity to small changes is transformed into a useful robustness property through adversarial training.
2Reliability
If adversarial training data samples are generated to maximize embedding distance, then the robust encoder can minimize embedding differences, but the training process becomes more complex
Solution Approach 1:
The patent performs preliminary action by pre-computing adversarial training data samples using a simplified initial encoder before training the robust encoder. This separates the complex adversarial sample generation from the main training process, reducing the computational burden during encoder training while still achieving robust embeddings.
Solution Approach 2:
The patent uses copying by initializing the robust encoder with the parameters of the initial encoder that generated the adversarial samples. This allows the robust encoder to inherit the initial encoder's knowledge and structure, reducing the complexity of training from scratch while still improving robustness through adversarial examples.
Data Source
AI summary
A computer model (e.g., an artificial intelligence model) having an encoder that generates embeddings may undesirably generate relatively large differences in embeddings for small changes in the input data sample. To improve robustness of the model against this type of change, training samples may be modified to generate adversarial examples that have comparatively large embedding differences relative to the change in training data sample. The adversarial data samples may be generated iteratively by exploring perturbations of the training data sample within a threshold to increase the distance in the embedding space. A robust encoder for the model may then be trained with the training data sample and adversarial data sample to reduce the distance between the corresponding training embedding and adversarial embedding.