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# Quantization Edit

100 papers with code · Methodology

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# Unsupervised Neural Quantization for Compressed-Domain Similarity Search

11 Aug 2019stanis-morozov/unq

We tackle the problem of unsupervised visual descriptors compression, which is a key ingredient of large-scale image retrieval systems.

3
11 Aug 2019

# Light Multi-segment Activation for Model Compression

16 Jul 2019LMA-NeurIPS19/LMA

Inspired by the nature of the expressiveness ability in Neural Networks, we propose to use multi-segment activation, which can significantly improve the expressiveness ability with very little cost, in the compact student model.

1
16 Jul 2019

# And the Bit Goes Down: Revisiting the Quantization of Neural Networks

In this paper, we address the problem of reducing the memory footprint of ResNet-like convolutional network architectures.

352
12 Jul 2019

# Don't take it lightly: Phasing optical random projections with unknown operators

3 Jul 2019swing-research/opu_phase

A signal of interest $\mathbf{\xi} \in \mathbb{R}^N$ is mixed by a random scattering medium to compute the projection $\mathbf{y} = \mathbf{A} \mathbf{\xi}$, with $\mathbf{A} \in \mathbb{C}^{M \times N}$ being a realization of a standard complex Gaussian iid random matrix.

0
03 Jul 2019

# Beyond Product Quantization: Deep Progressive Quantization for Image Retrieval

16 Jun 2019cfm-uestc/DPQ

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

4
16 Jun 2019

# Deep Recurrent Quantization for Generating Sequential Binary Codes

16 Jun 2019cfm-uestc/DRQ

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.

0
16 Jun 2019

# Deep Metric Learning to Rank

We propose a novel deep metric learning method by revisiting the learning to rank approach.

18
01 Jun 2019

# Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On Microcontrollers

30 May 2019mrusci/training-mixed-precision-quantized-networks

To fit the memory and computational limitations of resource-constrained edge-devices, we exploit mixed low-bitwidth compression, featuring 8, 4 or 2-bit uniform quantization, and we model the inference graph with integer-only operations.

2
30 May 2019

# Quantization-Based Regularization for Autoencoders

27 May 2019AlbertOh90/Soft-VQ-VAE

We show that our proposed regularization method results in improved latent representations for both supervised learning and clustering downstream tasks when compared to autoencoders using other bottleneck structures.

2
27 May 2019

# Additive Noise Annealing and Approximation Properties of Quantized Neural Networks

24 May 2019spallanzanimatteo/QuantLab

We present a theoretical and experimental investigation of the quantization problem for artificial neural networks.

2
24 May 2019