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ZHOUXINWEN's starred repositories

pytorch_wavelets

Pytorch implementation of 2D Discrete Wavelet (DWT) and Dual Tree Complex Wavelet Transforms (DTCWT) and a DTCWT based ScatterNet

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TimesNet

About Code release for "TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis" (ICLR 2023), https://openreview.net/pdf?id=ju_Uqw384Oq

MIT60700

SimTSC

[SDM 2022] Towards Similarity-Aware Time-Series Classification

Language:PythonMIT6600

tuning_playbook

A playbook for systematically maximizing the performance of deep learning models.

NOASSERTION2546500

resnet1d

PyTorch implementations of several SOTA backbone deep neural networks (such as ResNet, ResNeXt, RegNet) on one-dimensional (1D) signal/time-series data.

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UCR_Time_Series_Classification_Deep_Learning_Baseline

Fully Convlutional Neural Networks for state-of-the-art time series classification

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MCNN

PyTorch implementation of "Multi-scale Convolutional Neural Network for Time Series Classification - Cui et al. (2016)"

Language:Jupyter NotebookMIT600

MACNN-Pytorch

Unofficial implementation of paper “Multi-scale Attention Convolutional Neural Network for time series classification”[Neural Networks2021]

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Beyond3DMM

Beyond 3DMM Space: Towards Fine-grained 3D Face Reconstruction, ECCV, 2020

Language:PythonBSD-2-Clause10400

SimCC

[ECCV'2022 Oral] PyTorch implementation for: SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation (http://arxiv.org/abs/2107.03332). Old name: SimDR

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moviepy

Video editing with Python

Language:PythonMIT1196300

imagehash

A Python Perceptual Image Hashing Module

Language:PythonBSD-2-Clause304600

imgdupes

Identifying and removing near-duplicate images using perceptual hashing.

Language:Python33700

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

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