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
Binarized 3D Whole-body Human Mesh Recovery
zhitengli/bidrn
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In this work, we propose a Binarized Dual Residual Network (BiDRN), a novel quantization method to estimate the 3D human body, face, and hands parameters efficiently.
Video Instance Matting
shi-labs/vim
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To remedy this deficiency, we propose Video Instance Matting~(VIM), that is, estimating alpha mattes of each instance at each frame of a video sequence.
Deep Hashing via Householder Quantization
Hashing is at the heart of large-scale image similarity search, and recent methods have been substantially improved through deep learning techniques.
Persis: A Persian Font Recognition Pipeline Using Convolutional Neural Networks
What happens if we encounter a suitable font for our design work but do not know its name?
PB-LLM: Partially Binarized Large Language Models
This paper explores network binarization, a radical form of quantization, compressing model weights to a single bit, specifically for Large Language Models (LLMs) compression.
Class Binarization to NeuroEvolution for Multiclass Classification
In this paper, we apply class binarization techniques to a neuroevolution algorithm, NeuroEvolution of Augmenting Topologies (NEAT), that is used to generate neural networks for multiclass classification.
Estimator Meets Equilibrium Perspective: A Rectified Straight Through Estimator for Binary Neural Networks Training
The pioneering work BinaryConnect uses Straight Through Estimator (STE) to mimic the gradients of the sign function, but it also causes the crucial inconsistency problem.
BinaryViT: Pushing Binary Vision Transformers Towards Convolutional Models
With the increasing popularity and the increasing size of vision transformers (ViTs), there has been an increasing interest in making them more efficient and less computationally costly for deployment on edge devices with limited computing resources.
Binary domain generalization for sparsifying binary neural networks
Binary neural networks (BNNs) are an attractive solution for developing and deploying deep neural network (DNN)-based applications in resource constrained devices.
Feature Mixing for Writer Retrieval and Identification on Papyri Fragments
marco-peer/hip23
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This paper proposes a deep-learning-based approach to writer retrieval and identification for papyri, with a focus on identifying fragments associated with a specific writer and those corresponding to the same image.
