  Deep learning-based methods in computational microscopy have been shown to be
powerful but in general face some challenges due to limited generalization to
new types of samples and requirements for large and diverse training data.
Here, we demonstrate a few-shot transfer learning method that helps a
holographic image reconstruction deep neural network rapidly generalize to new
types of samples using small datasets. We pre-trained a convolutional recurrent
neural network on a large dataset with diverse types of samples, which serves
as the backbone model. By fixing the recurrent blocks and transferring the rest
of the convolutional blocks of the pre-trained model, we reduced the number of
trainable parameters by ~90% compared with standard transfer learning, while
achieving equivalent generalization. We validated the effectiveness of this
approach by successfully generalizing to new types of samples using small
holographic datasets for training, and achieved (i) ~2.5-fold convergence speed
acceleration, (ii) ~20% computation time reduction per epoch, and (iii)
improved reconstruction performance over baseline network models trained from
scratch. This few-shot transfer learning approach can potentially be applied in
other microscopic imaging methods, helping to generalize to new types of
samples without the need for extensive training time and data.
