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Domain Adaptation for Time Series Under Feature and Label Shifts

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In the correct function in `algorithms/RAINCOAT.py`, `src_y` in the incoming arguments is not called, and the correct process is no different from the update training process. ``` def correct(self, src_x,...

Hi, I have some questions about Section "C.2. Experimental Details", in which you provide the key hyperparameters of the RAINCOAT algorithm. I noticed that in Table 5, 6, 7, and...

https://github.com/mims-harvard/Raincoat/tree/main/trainers/trainer_uni.py # Paper Algorithm 1. Overview of RAINCOAT [Stage 3 Inference] ` c_list = self.learn_t(dis2proto_a, dis2proto_c) print(c_list) self.trg_true_labels = tar_uni_label_test acc, f1, H = self.detect_private(dis2proto_a_test, dis2proto_c_test, tar_uni_label_test, c_list) ` ##...

For the Sleep-EDF dataset, the following error is reported: Traceback (most recent call last): File "/media/qiu/DataDisk/yue/Raincoat-main (1)/Raincoat-main/main.py", line 37, in trainer.train() File "/media/qiu/DataDisk/yue/Raincoat-main (1)/Raincoat-main/trainers/trainer.py", line 113, in train losses =...

I used the Sleep_EDF dataset (EEG) to run the code in the author's outline. Some bugs were found: - In the data_model_configs.py, for EEG dataset, the stride is set to...

Great Work on the Time series enhanced by frequency information🤩! I noticed that you did not use `ifft` to change back in time domain, which makes me curious about the...

Does the code implementation contains inference step, especially source prototype part and bimodel test to check those unknown samples? It seems the RAINCOAT algorithm only implements alignment part and correction...