LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks — mentioned by p1esk

Tue, 14 Aug 2018 16:21:45 GMT

NN quantization has been an area of active research in the last 3 years, but it's not trivial when going to 4 bits or below. Usually to achieve good accuracy during inference, a model needs to be trained or finetuned at low precision. The simple post training conversion usually won't work (it does not always work even at 8 bits). Models that are already efficient (e.g. MobileNet) are harder to quantize than fat, overparameterized models such as AlexNet or VGG. Increasing a model size (number of neurons or filters) helps, but obviously it offsets the gains in efficiency to some degree.…

Paper and discussion history. A mention is not an endorsement.

LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks · HackerPapers discussions

LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks — mentioned by p1esk