Regarding final classifier layer implementation

[dipamgoswami]

Hello,
Thanks for the well documented code.

I have a query regarding the final classifier layer used in SSUL implementation and the architecture mentioned in the paper. The paper states that the final layer consists of 1*1 filters, one for each output class and in incremental steps, only the 1*1 filters are learned.
But in the code, the classifier layer is as follows:
self.head = nn.ModuleList( [ nn.Sequential( nn.Conv2d(256, 256, 3, padding=1, bias=False), nn.BatchNorm2d(256), nn.ReLU(inplace=True), nn.Conv2d(256, c, 1) ) for c in num_classes] )
This entire head is learned for each new set of classes and the weight transfer is done for the entire head as well.

Can you clarify this ? Can I reproduce the results using only the final conv2d layer like self.head = [nn.Conv2d(256, c, 1) for c in num_classes] ?