Image Embeddings With Adaptive Batch Normalization for Batch Effects
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Solution Overview
Problem
Conventional techniques for generating image embeddings fail to account for batch effects, leading to noise and inaccurate representation of visual attributes in images captured under varying conditions.
Innovation Solution
Implement adaptive batch normalization in a machine learning model to normalize outputs based on statistics computed from batches of images captured under different conditions, reducing noise and enhancing the accuracy of image embeddings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional neural networks are trained to generate image embeddings, then the embeddings can characterize and compare images, but batch effects cause noise and inaccurate representation of visual attributes
Solution Approach 1:
The patent introduces batch normalization as an intermediary mechanism between the neural network layers and the image embeddings. This normalization layer acts as a mediator that computes statistics (mean and standard deviation) from the batch of images and uses these statistics to normalize the activations, thereby removing batch effects before the embeddings are generated. This directly addresses the harmful batch effects while preserving the accurate characterization capability.
Solution Approach 2:
The patent changes the parameters of the neural network by adding batch normalization layers that compute and apply batch-specific statistics (mean and standard deviation) as parameters. These dynamically computed parameters allow the network to adapt to different batch conditions and remove batch effects from the embeddings, thereby improving measurement precision without sacrificing the ability to characterize visual attributes accurately.
2Adaptability or versatility
If the training dataset includes multiple batches of images from multiple experiments, then the network can learn from diverse conditions, but it cannot capture all possible combinations of conditions that cause batch effects
Solution Approach 1:
The patent makes the normalization parameters dynamic by computing them from the actual batch data rather than using fixed pre-computed statistics. This dynamic batch normalization allows the network to adapt to any batch condition, including combinations not seen during training, because the statistics are recalculated based on the specific batch's characteristics. This resolves the contradiction by enabling both adaptability to diverse conditions and reliable generalization to unseen conditions.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for training a machine learning model to generate image embeddings for images captured during multiple experiments. The technique includes inputting a batch of images into a plurality of layers in the machine learning model, wherein the batch of images has been sampled from a plurality of images generated via a first experiment. The technique also includes, for at least one layer included in the plurality of layers, computing a set of statistics associated with a plurality of outputs generated by the layer based on the batch of images and normalizing the plurality of outputs based on the statistics. The technique further includes updating a plurality of parameters for each of the plurality of layers based on a set of predictions generated by the first machine learning model from the batch of images and the normalized plurality of outputs.