Binarization
147 papers with code • 16 benchmarks • 17 datasets
Benchmarks
These leaderboards are used to track progress in Binarization
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Libraries
Use these libraries to find Binarization models and implementationsDatasets
CIFAR-10
ImageNet
CIFAR-100
JHU-CROWD
FDST
Most implemented papers
HashNet: Deep Learning to Hash by Continuation
Learning to hash has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval, due to its computation efficiency and retrieval quality.
A selectional auto-encoder approach for document image binarization
ajgallego/document-image-binarization
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Binarization plays a key role in the automatic information retrieval from document images.
Classification is a Strong Baseline for Deep Metric Learning
azgo14/classification_metric_learning
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Deep metric learning aims to learn a function mapping image pixels to embedding feature vectors that model the similarity between images.
Forward and Backward Information Retention for Accurate Binary Neural Networks
Our empirical study indicates that the quantization brings information loss in both forward and backward propagation, which is the bottleneck of training accurate binary neural networks.
LUTNet: Learning FPGA Configurations for Highly Efficient Neural Network Inference
awai54st/LUTNet
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Research has shown that deep neural networks contain significant redundancy, and thus that high classification accuracy can be achieved even when weights and activations are quantized down to binary values.
Neural Network Compression Framework for fast model inference
openvinotoolkit/nncf_pytorch
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In this work we present a new framework for neural networks compression with fine-tuning, which we called Neural Network Compression Framework (NNCF).
BiDet: An Efficient Binarized Object Detector
ZiweiWangTHU/BiDet
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CVPR 2020
Conventional network binarization methods directly quantize the weights and activations in one-stage or two-stage detectors with constrained representational capacity, so that the information redundancy in the networks causes numerous false positives and degrades the performance significantly.
Binary Neural Networks: A Survey
htqin/awesome-model-quantization
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The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices.
Rotated Binary Neural Network
In this paper, for the first time, we explore the influence of angular bias on the quantization error and then introduce a Rotated Binary Neural Network (RBNN), which considers the angle alignment between the full-precision weight vector and its binarized version.
FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional Activations
We design an efficient FPGA-based accelerator for our novel BNN model that supports the fractional activations.
