Interpret A Quantized Network Visually Youtube Learn what interpretability features are available for quantized and pruned networks in matlab®. visualization methods can explain network predictions using. “since pretrained neural network weights usually have a zero centered normal distribution” [16] normalfloat4 tackles this by assuming that the values to be quantized i.e. the values in x come from a normal distribution. the is taken and normalized by the absmax (max of the absolute values) so that the values now all fall between [ 1, 1].
Explanation Of How To Interpret The Network Visualizations Reprinted Part 2: introduction to quantization. quantization aims to reduce the precision of a model’s parameter from higher bit widths (like 32 bit floating point) to lower bit widths (like 8 bit integers). there is often some loss of precision (granularity) when reducing the number of bits to represent the original parameters. To our knowl edge, these are the first fully quantized 4 bit object de tection models that achieve acceptable accuracy loss and requires no special hardware design, and thus may be used as a baseline for future end to end low bit quantization schemes on complex tasks. 2. related works. 2.1. modern detectors. Visualization methods are used to explain network predictions via visual representations of what a quantized network is looking at. …see more interpret a quantized network visually. We apply our techniques to produce fully quantized 4 bit detectors based on retinanet and faster r cnn, and show that these achieve state of the art performance for quantized detectors. the map loss due to quantization using our methods is more than 3.8x less than the loss from existing methods. pdf abstract.
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