GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks. (arXiv:2208.12489v1 [cs.LG])

Deep convolutional neural network (CNN) training via iterative optimization
has had incredible success in finding optimal parameters. However, modern CNN
architectures often contain millions of parameters. Thus, any given model for a
single architecture resides in a massive parameter space. Models with similar
loss could have drastically different characteristics such as adversarial
robustness, generalizability, and quantization robustness. For deep learning on
the edge, quantization robustness is often crucial. Finding a model that is
quantization-robust can sometimes require significant efforts. Recent …

arxiv graph lg prediction