Feature-wise transformations A simple simple research paper surprisingly effective family of conditioning mechanisms.
Many real-world problems require integrating multiple sources of information. Other times, these problems involve multiple sources of the same kind of input, i. Finding an effective way to condition on or fuse sources of information simple research paper an open research problem, and in this article, we concentrate on a specific family of approaches we call feature-wise transformations. Now let’s imagine that, in addition to the various classes, we also need to model attributes like size or color. In this case, we can’t reasonably expect to train a separate network for each possible conditioning combination! Let’s examine a few simple options. A quick fix would be to concatenate a representation of the conditioning information to the noise vector and treat the result as the model’s input.
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This solution is quite parameter-efficient, as we only need to increase the size of the first layer’s weight matrix. However, this approach makes the implicit assumption that the input is where the model needs to use the conditioning information. Because this operation is cheap, we might as well avoid making any such assumptions and concatenate the conditioning representation to the input of all layers in the network. Let’s call this approach concatenation-based conditioning. The same argument applies to convolutional networks, provided we ignore the border effects due to zero-padding. M 0 0 C 1 2.
Intuitively, this gating allows the conditioning information to select which features are passed forward and which are zeroed out. Given that both additive and multiplicative interactions seem natural and intuitive, which approach should we pick? This property is why dot products are often used to determine how similar two vectors are. In the spirit of making as few assumptions about the problem as possible, we may as well combine both into a conditional affine transformation. Lastly, these transformations only enforce a limited inductive bias and remain domain-agnostic. This quality can be a downside, as some problems may be easier to solve with a stronger inductive bias.
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