Temporal Convolutions

Causal Convolution

Introduced by Oord et al. in WaveNet: A Generative Model for Raw Audio

Causal convolutions are a type of convolution used for temporal data which ensures the model cannot violate the ordering in which we model the data: the prediction $p(x_{t+1} | x_{1}, \ldots, x_{t})$ emitted by the model at timestep $t$ cannot depend on any of the future timesteps $x_{t+1}, x_{t+2}, \ldots, x_{T}$. For images, the equivalent of a causal convolution is a masked convolution which can be implemented by constructing a mask tensor and doing an element-wise multiplication of this mask with the convolution kernel before applying it. For 1-D data such as audio one can more easily implement this by shifting the output of a normal convolution by a few timesteps.

Source: WaveNet: A Generative Model for Raw Audio


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🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign