Hi, I am studying your research and have a few questions regarding your code.
In Section 2.6.3, "Utilizing hidden representations for the anomaly detection task," you took the blue bar from Fig. 3 and apply a 1 x 1 convolution layer to reduce the channel size from 16 (as shown in the figure) to 1.
However, from the provided explanation, it seems the 1 x 1 convolution layer is not included in the training loop. I am curious if using an untrained convolution layer to reduce the feature dimension is feasible or if it might negatively impact performance.
Additionally, in Section 2.6.4, "Feature map reduction," you place 1 x 1 convolutions after each dilated convolutional layer to reduce the feature map dimension. Based on Issue #3, my understanding is that these layers are optionally included in the training loop. Typically, adding additional layers (such as 1 x 1 convolutions) would increase training time and the number of parameters.
Could you please explain how adding these additional layers (1 x 1 convolutions) helps in reducing trainable parameters and training time?
Hi, I am studying your research and have a few questions regarding your code.
In Section 2.6.3, "Utilizing hidden representations for the anomaly detection task," you took the blue bar from Fig. 3 and apply a 1 x 1 convolution layer to reduce the channel size from 16 (as shown in the figure) to 1.
However, from the provided explanation, it seems the 1 x 1 convolution layer is not included in the training loop. I am curious if using an untrained convolution layer to reduce the feature dimension is feasible or if it might negatively impact performance.
Additionally, in Section 2.6.4, "Feature map reduction," you place 1 x 1 convolutions after each dilated convolutional layer to reduce the feature map dimension. Based on Issue #3, my understanding is that these layers are optionally included in the training loop. Typically, adding additional layers (such as 1 x 1 convolutions) would increase training time and the number of parameters.
Could you please explain how adding these additional layers (1 x 1 convolutions) helps in reducing trainable parameters and training time?
Could you also provide the code for this part?
Thank you for your help :)