WXinlong / DenseCL

Dense Contrastive Learning (DenseCL) for self-supervised representation learning, CVPR 2021 Oral.
GNU General Public License v3.0
549 stars 70 forks source link

DenseNeck design #24

Open hao-pt opened 3 years ago

hao-pt commented 3 years ago

Have you tried different output channels for single projection and dense projection? Particularly, you used the same hidden channels and output channels for single mlp and dense mlp in the DenseCLNeck impl. As I know, the projection of instance representation requires a greater number of channels than the projection of dense representation. Treating both of them equally might lose lots of useful information from instance representation. How do you think about this problem? Most instance discrimination methods also design the projector as fc-bn-relu-fc so I wonder why you drop bn in DenseCLNeck? Is it just for simplicity?

        self.mlp = nn.Sequential(
            nn.Linear(in_channels, hid_channels), nn.ReLU(inplace=True),
            nn.Linear(hid_channels, out_channels))
        ...
        self.mlp2 = nn.Sequential(
            nn.Conv2d(in_channels, hid_channels, 1), nn.ReLU(inplace=True),
            nn.Conv2d(hid_channels, out_channels, 1))