What enables the ImageNet pretrained models to learn useful audio representations, we systematically study how much of pretrained weights is useful for learning spectrograms.
We show
- (1) that for a given standard model using pretrianed weights is better than using randomly initialized weights
- (2) qualitative results of what the CNNs learn from the spectrograms by visualizing the gradients.
Besides, wer show that even though we use the pretrained model weights for initialization, there is variance in performance in various output runs of the same model. This variance in performance is due to the random initilaization of linear classification layer and random mini-batch orderings in multiple runs.
标签：Rethinking,useful,show,random,weights,CNN,model,Audio,pretrained From： https://www.cnblogs.com/prettysky/p/17159633.html