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Found 6 papers, 3 papers with code
QGen: On the Ability to Generalize in Quantization Aware Training
In this work, we investigate the generalization properties of quantized neural networks, a characteristic that has received little attention despite its implications on model performance.
QReg: On Regularization Effects of Quantization
To confirm our analytical study, we performed an extensive list of experiments summarized in this paper in which we show that the regularization effects of quantization can be seen in various vision tasks and models, over various datasets.
iPrune: A Magnitude Based Unstructured Pruning Method for Efficient Binary Networks in Hardware
Compared to very recent work on pruning for binary networks, we still have a gain of 1% on the precision and up to 30% reduction in memory (526KiB vs 750KiB).
U-Net Fixed-Point Quantization for Medical Image Segmentation
We then apply our quantization algorithm to three datasets: (1) the Spinal Cord Gray Matter Segmentation (GM), (2) the ISBI challenge for segmentation of neuronal structures in Electron Microscopic (EM), and (3) the public National Institute of Health (NIH) dataset for pancreas segmentation in abdominal CT scans.
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
We introduce BinaryConnect, a method which consists in training a DNN with binary weights during the forward and backward propagations, while retaining precision of the stored weights in which gradients are accumulated.
Ranked #30 on
Image Classification
on SVHN
Training deep neural networks with low precision multiplications
For each of those datasets and for each of those formats, we assess the impact of the precision of the multiplications on the final error after training.
