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Dynamic Time Warping

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/SPEECHCOG/PDTW

Unsupervised Discovery of Recurring Speech Patterns Using Probabilistic Adaptive Metrics

https://github.com/Zepp3/Master-Thesis

Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative Teacher

https://github.com/aidatalab314/AUO_forecasting

Multi-Scale Convolutional Neural Networks for Time Series Classification

https://github.com/alessandroragano/nomad

WARP-Q: Quality Prediction For Generative Neural Speech Codecs

https://github.com/baggepinnen/DynamicAxisWarping.jl

Soft-DTW: a Differentiable Loss Function for Time-Series

https://github.com/camma-public/peskavlp

Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation

https://github.com/camma-public/surgvlp

Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation

https://github.com/cran/VLTimeCausality

Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis

https://github.com/dhesenkamp/attentive-lstm

Human Motion Analysis with Deep Metric Learning

https://github.com/earthspecies/audio-embeddings

Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Autoencoder

https://github.com/google-research-datasets/RxR

General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping

https://github.com/jihyeonseong/SoM-TP

Soft-DTW: a Differentiable Loss Function for Time-Series

https://github.com/kamperh/recipe_semantic_flickraudio

Query-by-Example Search with Discriminative Neural Acoustic Word Embeddings

https://github.com/keonlee9420/Soft-DTW-Loss

Soft-DTW: a Differentiable Loss Function for Time-Series

https://github.com/keonlee9420/WaveGrad2

End-to-End Adversarial Text-to-Speech

https://github.com/khdlr/softdtw_jax

Soft-DTW: a Differentiable Loss Function for Time-Series

https://github.com/kirarenctaon/timenet

TimeNet: Pre-trained deep recurrent neural network for time series classification

https://github.com/ktatarnikov/time-series

SSH (Sketch, Shingle, & Hash) for Indexing Massive-Scale Time Series

https://github.com/lhy-95/Exact-Discovery-of-Time-Series-Motifs-under-DTW

Matrix Profile XXII: Exact Discovery of Time Series Motifs under DTW

https://github.com/lyprince/sdtw_pytorch

Soft-DTW: a Differentiable Loss Function for Time-Series

https://github.com/marcdemers/batch-DILATE

Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models

https://github.com/mmr12/MGP-AttTCN

Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping

https://github.com/moradisten/KNN-LB

Elastic bands across the path: A new framework and methods to lower bound DTW

https://github.com/msd-irimas/domainfoundationmodelstsc

Bake off redux: a review and experimental evaluation of recent time series classification algorithms

https://github.com/neuralprophet/synthetic_prophet

Data augmentation using synthetic data for time series classification with deep residual networks

https://github.com/paudan/TimeNet

TimeNet: Pre-trained deep recurrent neural network for time series classification

https://github.com/pfmarteau/ShapeTimeSeparation

Times series averaging and denoising from a probabilistic perspective on time-elastic kernels

https://github.com/pfmarteau/eKATS

Times series averaging and denoising from a probabilistic perspective on time-elastic kernels

https://github.com/rpeloff/multimodal_one_shot_learning

Multimodal One-Shot Learning of Speech and Images

https://github.com/titu1994/Adversarial-Attacks-Time-Series

Adversarial Attacks on Time Series

https://github.com/weirdlabuw/strap

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning

https://github.com/xrenaa/Human-Motion-Analysis-with-Deep-Metric-Learning

Human Motion Analysis with Deep Metric Learning

https://github.com/yanggeng1995/EATS

End-to-End Adversarial Text-to-Speech

https://github.com/yueyaog/TSGEM_Clustering

Soft-DTW: a Differentiable Loss Function for Time-Series

https://github.com/zdcuob/Fully-Convlutional-Neural-Networks-for-state-of-the-art-time-series-classification-

Multi-Scale Convolutional Neural Networks for Time Series Classification

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.