TensorNetwork for Machine Learning
This work introduces a tensor network-based method for image classification, offering a novel approach with competitive performance on standard datasets.
The paper tackles image classification by applying tensor networks to encode image data into matrix product states, achieving 98% accuracy on MNIST and 88% on Fashion-MNIST with the same architecture.
We demonstrate the use of tensor networks for image classification with the TensorNetwork open source library. We explain in detail the encoding of image data into a matrix product state form, and describe how to contract the network in a way that is parallelizable and well-suited to automatic gradients for optimization. Applying the technique to the MNIST and Fashion-MNIST datasets we find out-of-the-box performance of 98% and 88% accuracy, respectively, using the same tensor network architecture. The TensorNetwork library allows us to seamlessly move from CPU to GPU hardware, and we see a factor of more than 10 improvement in computational speed using a GPU.