Binarization
128 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
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
hpi-xnor/BMXNet
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We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks.
Real-time Scene Text Detection with Differentiable Binarization
MhLiao/DB
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Recently, segmentation-based methods are quite popular in scene text detection, as the segmentation results can more accurately describe scene text of various shapes such as curve text.
Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion
MhLiao/DB
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By incorporating the proposed DB and ASF with the segmentation network, our proposed scene text detector consistently achieves state-of-the-art results, in terms of both detection accuracy and speed, on five standard benchmarks.
READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents
Well established text line segmentation evaluation schemes such as the Detection Rate or Recognition Accuracy demand for binarized data that is annotated on a pixel level.
Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources
1adrianb/binary-networks-pytorch
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ICCV 2017
(d) We present results for experiments on the most challenging datasets for human pose estimation and face alignment, reporting in many cases state-of-the-art performance.
Towards the first adversarially robust neural network model on MNIST
bethgelab/AnalysisBySynthesis
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ICLR 2019
Despite much effort, deep neural networks remain highly susceptible to tiny input perturbations and even for MNIST, one of the most common toy datasets in computer vision, no neural network model exists for which adversarial perturbations are large and make semantic sense to humans.
Adaptive Image Sampling using Deep Learning and its Application on X-Ray Fluorescence Image Reconstruction
usstdqq/deep-adaptive-sampling-mask
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We propose an XRF image inpainting approach to address the issue of long scanning time, thus speeding up the scanning process while still maintaining the possibility to reconstruct a high quality XRF image.
DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement
dali92002/DE-GAN
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Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system.
ReCU: Reviving the Dead Weights in Binary Neural Networks
z-hXu/ReCU
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ICCV 2021
We prove that reviving the "dead weights" by ReCU can result in a smaller quantization error.
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.
