Popular GitHub Repositories for Spectral Normalization

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

Microsoft.Recognizers.Text provides recognition and resolution of numbers, units, date/time, etc. in multiple languages (ZH, EN, FR, ES, PT, DE, IT, TR, HI, NL. Partial support for JA, KO, AR, SV). Packages available at: https://www.nuget.org/profiles/Recognizers.Text, https://www.npmjs.com/~recognizers.text

Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Persian NLP Toolkit

Unofficial pytorch implementation of 'Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization' [Huang+, ICCV2017]

a pytorch lib with state-of-the-art architectures, pretrained models and real-time updated results

A toolbox for spectral compressive imaging reconstruction including MST (CVPR 2022), CST (ECCV 2022), DAUHST (NeurIPS 2022), BiSCI (NeurIPS 2023), HDNet (CVPR 2022), MST++ (CVPRW 2022), etc.

Chinese text normalization for speech processing

SEAN: Image Synthesis with Semantic Region-Adaptive Normalization (CVPR 2020, Oral)

Python re-implementation of the (constrained) spectral clustering algorithms used in Google's speaker diarization papers.

The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.

Deep learning library in plain Numpy.

Quick implementation of nGPT, learning entirely on the hypersphere, from NvidiaAI

The official repo for the paper "HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model"

Experimental results obtained with the MinCutPool layer as presented in the 2020 ICML paper "Spectral Clustering with Graph Neural Networks for Graph Pooling"

List of resources for mineral exploration and machine learning, generally with useful code and examples.