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 Pytorch implementation source coder for paper [Robust Online Model Adaptation by Extended Kalman Filter with Exponential Moving Average and Dynamic Multi-Epoch Strategy](https://arxiv.org/abs/1912.01790).
 
 
-Inspired by Extended Kalman Filter (EKF), a base adaptation algorithm Modified EKF with forgetting
-factor (MEKF_$\lambda$) is introduced first. Using exponential moving average (EMA) methods, this
-paper proposes EMA filtering to the base EKF<sub>λ</sub> in order to increase the convergence rate. followed by exponential moving average filtering techniques.
-Then in order to effectively utilize the samples in online
-adaptation, this paper proposes a dynamic multi-epoch update strategy to discriminate the “hard”
-samples from “easy” samples, and sets different weights for them.  With all these extensions, we propose a robust online adaptation algorithm:
-MEKF with Exponential Moving Average and Dynamic Multi-Epoch strategy (MEKF<sub>EMA-DME</sub>).
+In this paper, inspired by Extended Kalman Filter (EKF), a base adaptation algorithm Modified EKF with forgetting
+factor (MEKF<sub>λ</sub>) is introduced first. Then using exponential moving average (EMA) methods, this
+paper proposes EMA filtering to the base EKF<sub>λ</sub> in order to increase the convergence rate. 
+In order to effectively utilize the samples in online adaptation, this paper proposes a dynamic multi-epoch update strategy to discriminate the “hard” samples from “easy” samples, and sets different weights for them.  With all these extensions, this paper proposes a robust online adaptation algorithm: MEKF with Exponential Moving Average and Dynamic Multi-Epoch update strategy (MEKF<sub>EMA-DME</sub>).