**DNN2LR** is an automatic feature crossing method to find feature interactions in a deep neural network, and use them as cross features in logistic regression. In general, DNN2LR consists of two steps: (1) generating a compact and accurate candidate set of cross feature fields; (2) searching in the candidate set for the final cross feature fields.

**Conditional Positional Encoding**, or **CPE**, is a type of positional encoding for [vision transformers](https://paperswithcode.com/methods/category/vision-transformer). Unlike previous fixed or learnable positional encodings, which are predefined and independent of input tokens, CPE is dynamically generated and conditioned on the local neighborhood of the input tokens. As a result, CPE aims to generalize to the input sequences that are longer than what the model has ever seen during training. CPE can also keep the desired translation-invariance in the image classification task. CPE can be implemented with a [Position
Encoding Generator](https://paperswithcode.com/method/positional-encoding-generator) (PEG) and incorporated into the current [Transformer framework](https://paperswithcode.com/methods/category/transformers).

Conditional Positional Encoding

Conditional Positional Encodings for Vision Transformers

DNN2LR

DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data

**ColorJitter** is a type of image data augmentation where we randomly change the brightness, contrast and saturation of an image.

Image Credit: [Apache MXNet](https://mxnet.apache.org/versions/1.5.0/tutorials/gluon/data_augmentation.html)

Source	DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com