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Quantization

93 papers with code ยท Methodology

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Back to Simplicity: How to Train Accurate BNNs from Scratch?

19 Jun 2019

Binary Neural Networks (BNNs) show promising progress in reducing computational and memory costs but suffer from substantial accuracy degradation compared to their real-valued counterparts on large-scale datasets, e. g., ImageNet.

QUANTIZATION

Deep Learning-Based Quantization of L-Values for Gray-Coded Modulation

18 Jun 2019

In this work, a deep learning-based quantization scheme for log-likelihood ratio (L-value) storage is introduced.

QUANTIZATION

Deep Recurrent Quantization for Generating Sequential Binary Codes

16 Jun 2019

To the end, when the model is trained, a sequence of binary codes can be generated and the code length can be easily controlled by adjusting the number of recurrent iterations.

IMAGE RETRIEVAL QUANTIZATION

Beyond Product Quantization: Deep Progressive Quantization for Image Retrieval

16 Jun 2019

In this work, we propose a deep progressive quantization (DPQ) model, as an alternative to PQ, for large scale image retrieval.

IMAGE RETRIEVAL QUANTIZATION

Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks

14 Jun 2019

The deep layers of modern neural networks extract a rather rich set of features as an input propagates through the network.

QUANTIZATION

Parameterized Structured Pruning for Deep Neural Networks

12 Jun 2019

As a result, PSP maintains prediction performance, creates a substantial amount of sparsity that is structured and, thus, easy and efficient to map to a variety of massively parallel processors, which are mandatory for utmost compute power and energy efficiency.

QUANTIZATION

Data-Free Quantization through Weight Equalization and Bias Correction

11 Jun 2019

We introduce a data-free quantization method for deep neural networks that does not require fine-tuning or hyperparameter selection.

OBJECT DETECTION QUANTIZATION SEMANTIC SEGMENTATION

BasisConv: A method for compressed representation and learning in CNNs

11 Jun 2019

Specifically, any convolution layer of the CNN is easily replaced by two successive convolution layers: the first is a set of fixed filters (that represent the knowledge space of the entire layer and do not change), which is followed by a layer of one-dimensional filters (that represent the learned knowledge in this space).

QUANTIZATION

Table-Based Neural Units: Fully Quantizing Networks for Multiply-Free Inference

11 Jun 2019

We show results that are within 1. 6% of the reported, non-quantized performance on MobileNet using only 40 entries in our table.

QUANTIZATION

Fighting Quantization Bias With Bias

7 Jun 2019

In this paper, we trace the source of the degradation in MobileNets to a shift in the mean activation value.

QUANTIZATION