Binarization
150 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
Neural Network Compression using Binarization and Few Full-Precision Weights
Quantization and pruning are two effective Deep Neural Networks model compression methods.
Binary Radiance Fields
In this paper, we propose \textit{binary radiance fields} (BiRF), a storage-efficient radiance field representation employing binary feature encoding that encodes local features using binary encoding parameters in a format of either $+1$ or $-1$.
LDEB -- Label Digitization with Emotion Binarization and Machine Learning for Emotion Recognition in Conversational Dialogues
This entanglement that can be multiplied with the presence of data paucity is an obstacle for a ML model.
BinaryViT: Towards Efficient and Accurate Binary Vision Transformers
In this paper, we first argue empirically that the severe performance degradation is mainly caused by the weight oscillation in the binarization training and the information distortion in the activation of ViTs.
Bi-ViT: Pushing the Limit of Vision Transformer Quantization
Vision transformers (ViTs) quantization offers a promising prospect to facilitate deploying large pre-trained networks on resource-limited devices.
Input Layer Binarization with Bit-Plane Encoding
Binary Neural Networks (BNNs) use 1-bit weights and activations to efficiently execute deep convolutional neural networks on edge devices.
Binary stochasticity enabled highly efficient neuromorphic deep learning achieves better-than-software accuracy
We propose a binary stochastic learning algorithm that modifies all elementary neural network operations, by introducing (i) stochastic binarization of both the forwarding signals and the activation function derivatives, (ii) signed binarization of the backpropagating errors, and (iii) step-wised weight updates.
Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning'
Chen et al. [Chen2022] recently published the article 'Fast and scalable search of whole-slide images via self-supervised deep learning' in Nature Biomedical Engineering.
Binarizing Sparse Convolutional Networks for Efficient Point Cloud Analysis
In this paper, we propose binary sparse convolutional networks called BSC-Net for efficient point cloud analysis.
Compacting Binary Neural Networks by Sparse Kernel Selection
Binary Neural Network (BNN) represents convolution weights with 1-bit values, which enhances the efficiency of storage and computation.
