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Optimal channel selection with discrete QCQP
We first show the greedy approach of recent channel pruning methods ignores the inherent quadratic coupling between channels in the neighboring layers and cannot safely remove inactive weights during the pruning procedure.
RaScaNet: Learning Tiny Models by Raster-Scanning Images
RaScaNet reads only a few rows of pixels at a time using a convolutional neural network and then sequentially learns the representation of the whole image using a recurrent neural network.
Succinct Network Channel and Spatial Pruning via Discrete Variable QCQP
Reducing the heavy computational cost of large convolutional neural networks is crucial when deploying the networks to resource-constrained environments.
Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss
We demonstrate the effectiveness of our trainable quantizer on ImageNet dataset with various network architectures such as ResNet-18, -34 and AlexNet, on which it outperforms existing methods to achieve the state-of-the-art accuracy.
