arXiv:1912.08914
공식 코드 · Papers with Code
On the Metrics and Adaptation Methods for Domain Divergences of sEMG-based Gesture Recognition
원문 초록
We propose a new metric to measure domain divergence and a new domain adaptation method for time-series classification. The metric belongs to the class of probability distributions-based metrics, is transductive, and does not assume the presence of source data samples. The 2-stage method utilizes an improved autoregressive, RNN-based architecture with deep/non-linear transformation. We assess our metric and the performance of our model in the context of sEMG/EMG-based gesture recognition under inter-session and inter-subject domain shifts.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?
A Joint-Entropy Approach To Time-series Classification
Semi-supervised Time Series Classification by Temporal Relation Prediction
Source Model Selection for Deep Learning in the Time Series Domain
Support Vector Machines with Time Series Distance Kernels for Action Classification
TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.