Presumably, you have the seen the Distil article discussing checkerboard artifacts due to transposed convolutional layers, where they recommend upsampling (resizing) and then doing regular convolution. This is known to be a reasonable approach, so it is likely something specific to your case. Without architectural details, I can only guess that it might be (a) your hyper-parameters have been tuned to work with transposed conv layers, so it breaks with this different approach, or (b) you have introduced some kind of vanishing gradients or convergence issue (e.g., not using residual connections, not using batch norm, too large of a learning rate, etc...). The loss (e.g., $$L_2$$ reconstruction) and it's gradients may be of a different magnitude than what the other (hyper-)parameters were expecting, for example. Feel free to post back with more info.