**Mechanism Transfer** is a meta-distributional scenario for few-shot domain adaptation in which a data generating mechanism is invariant across domains. This transfer assumption can accommodate nonparametric shifts resulting in apparently different distributions while providing a solid statistical basis for domain adaptation.

An **Active Convolution** is a type of [convolution](https://paperswithcode.com/method/convolution) which does not have a fixed shape of the receptive field, and can be used to take more diverse forms of receptive fields for convolutions. Its shape can be learned through backpropagation during training. It can be seen as a generalization of convolution; it can define not only all conventional convolutions, but also convolutions with fractional pixel coordinates. We can freely change the shape of the convolution, which provides greater freedom to form CNN structures. Second, the shape of the convolution is learned while training and there is no need to tune it by hand

Active Convolution

Active Convolution: Learning the Shape of Convolution for Image Classification

Mechanism Transfer

Few-shot Domain Adaptation by Causal Mechanism Transfer

FT-Transformer (Feature Tokenizer + Transformer) is a simple adaptation of the [Transformer](/method/transformer) architecture for the tabular domain. The model (Feature Tokenizer component) transforms all features (categorical and numerical) to tokens and runs a stack of Transformer layers over the tokens, so every Transformer layer operates on the feature level of one object. (This model is similar to [AutoInt](/method/autoint)). In the Transformer component, the `[CLS]` token is appended to $T$. Then $L$ Transformer layers are applied. PreNorm is used for easier optimization and good performance. The final representation of the `[CLS]` token is used for prediction.

Source	Few-shot Domain Adaptation by Causal Mechanism Transfer
Year	2000
Data Source	Papers with Code archive (CC BY-SA)