Quantum Tensor Network in Machine Learning: An Application to Tiny Object Classification
This work tackles the problem of inefficient tiny object classification for machine learning applications where objects occupy small image regions, offering an incremental solution.
This paper addresses the challenge of classifying tiny objects in images, a common problem in medical imaging and remote sensing. The authors propose using a 2D multi-scale entanglement renormalization ansatz (MERA) from quantum tensor networks, demonstrating its effectiveness for tiny object classification and suggesting its potential to surpass current state-of-the-art methods.
Tiny object classification problem exists in many machine learning applications like medical imaging or remote sensing, where the object of interest usually occupies a small region of the whole image. It is challenging to design an efficient machine learning model with respect to tiny object of interest. Current neural network structures are unable to deal with tiny object efficiently because they are mainly developed for images featured by large scale objects. However, in quantum physics, there is a great theoretical foundation guiding us to analyze the target function for image classification regarding to specific objects size ratio. In our work, we apply Tensor Networks to solve this arising tough machine learning problem. First, we summarize the previous work that connects quantum spin model to image classification and bring the theory into the scenario of tiny object classification. Second, we propose using 2D multi-scale entanglement renormalization ansatz (MERA) to classify tiny objects in image. In the end, our experimental results indicate that tensor network models are effective for tiny object classification problem and potentially will beat state-of-the-art. Our codes will be available online https://github.com/timqqt/MERA_Image_Classification.