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
Latest papers with no code
DocStormer: Revitalizing Multi-Degraded Colored Document Images to Pristine PDF
Thus, we propose DocStormer, a novel algorithm designed to restore multi-degraded colored documents to their potential pristine PDF.
Using Logic Programming and Kernel-Grouping for Improving Interpretability of Convolutional Neural Networks
FOLD-SE-M then generates a rule-set that can be used to make predictions.
Dynamic Shuffle: An Efficient Channel Mixture Method
To reduce the data-dependent redundancy, we devise a dynamic shuffle module to generate data-dependent permutation matrices for shuffling.
A quantum segmentation algorithm based on local adaptive threshold for NEQR image
In this paper, a quantum segmentation algorithm based on local adaptive threshold for NEQR image is proposed, which can use quantum mechanism to simultaneously compute local thresholds for all pixels in a gray-scale image and quickly segment the image into a binary image.
A quantum moving target segmentation algorithm for grayscale video
For a quantum video with $2^m$ frames (every frame is a $2^n\times 2^n$ image with $q$ grayscale levels), the complexity of our algorithm can be reduced to O$(n^2 + q)$.
Gaining the Sparse Rewards by Exploring Lottery Tickets in Spiking Neural Network
Deploying energy-efficient deep learning algorithms on computational-limited devices, such as robots, is still a pressing issue for real-world applications.
Aggregating Credences into Beliefs: Agenda Conditions for Impossibility Results
Binarizing belief aggregation addresses how to rationally aggregate individual probabilistic beliefs into collective binary beliefs.
Learning Discrete Weights and Activations Using the Local Reparameterization Trick
In computer vision and machine learning, a crucial challenge is to lower the computation and memory demands for neural network inference.
Semantic Segmentation Using Super Resolution Technique as Pre-Processing
Combining high-level and low-level visual tasks is a common technique in the field of computer vision.
Neural Network Compression using Binarization and Few Full-Precision Weights
Quantization and pruning are two effective Deep Neural Networks model compression methods.
