Closed sdjsngs closed 3 years ago
Hi, yes! On that way, each weight indicates the impact of its partial score in the combination. The weight is defined as the inverse of the (average) score under the perspective that the lower score should give the higher impact (because the training data contain only frames of normality), and vice versa.
Hi , i am re-implement your paper in pytorch and has some question about W_i and W_f , in section 3.5 of your paper , the weight is obtained on the training data?
For Avenue dataset, when training is done (i.e. 15 epochs), you test the 16 training clips on G model through the 15 epoch's checkpoint to get the out_appe and out_flow , then to get the weight_I and weight_F in these 16 clips on average?
thank you !!!