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What is known so far comes from the other direction:
These projects were analysed and named flashlight among their alternatives. The relationship is not symmetric — how flashlight rates them is a separate judgement, made when flashlight is analysed in its own right.
calls flashlight “choose if you need a lightweight C++ machine learning library with no Python requirement for research or embedded settings.”
Tensors and Dynamic neural networks in Python with strong GPU acceleration
calls flashlight “Choose for C++ projects needing a standalone ML library with high performance.”
Deep Learning for humans
calls flashlight “when you want a lightweight C++ ML library for performance-critical, non-web deployment.”
A WebGL accelerated JavaScript library for training and deploying ML models.
calls flashlight “Choose when you need a C++-first ML library with optimized kernels and no Python dependency.”
Burn is a next generation tensor library and Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.
calls flashlight “choose when you need a standalone C++ ML library without a Python binding layer”
OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.
calls flashlight “When you want a lightweight C++ ML library for embedded or low-overhead training/inference.”
CoreNet: A library for training deep neural networks