I have pre-trained encoder (trained with only classification loss. no decoder and no reconstruct loss here)
If I flood input image to encoder, there will be latent space as output.
I want to see what the image looks like that the latent space can generate.

Is it impossible to construct image from latent space without training decoder?
Here is the pseudo code I think to do this.I’ not sure this way works fine.

encoder = model.encoder().cuda()
encoder.load_state_dict(torch.load('my_encoder.pt')

class New_Decoder(nn.Module):
 ...  Define decoder ...
   return out

decoder = Decoder().cuda()
...
enc_out = encoder(input_image)
dec_out = decoder(enc_out)
...


If trained decoder is needed.
I don’t want reconstruction loss affect to my model.
I want to use only classification loss to train the encoder.
Any way to do this?