**CTAB-GAN** is a model for conditional tabular data generation. The generator and discriminator utilize the [DCGAN](https://paperswithcode.com/method/dcgan) architecture. An [auxiliary classifier](https://paperswithcode.com/method/auxiliary-classifier) is also used with an MLP architecture.

**SimCSE** is a contrastive learning framework for generating sentence embeddings. It utilizes an unsupervised approach, which takes an input sentence and predicts itself in contrastive objective, with only standard [dropout](https://paperswithcode.com/method/dropout) used as noise. The authors find that dropout acts as minimal “data augmentation” of hidden representations, while removing it leads to a representation collapse. Afterwards a supervised approach is used, which incorporates annotated pairs from natural language inference datasets into the contrastive framework, by using “entailment” pairs as positives and “contradiction

SimCSE

SimCSE: Simple Contrastive Learning of Sentence Embeddings

CTAB-GAN

Effective and Privacy preserving Tabular Data Synthesizing

A **Selective Kernel** unit is a bottleneck block consisting of a sequence of 1×1 [convolution](https://paperswithcode.com/method/convolution), SK convolution and 1×1 convolution. It was proposed as part of the [SKNet](https://paperswithcode.com/method/sknet) CNN architecture. In general, all the large kernel convolutions in the original bottleneck blocks in [ResNeXt](https://paperswithcode.com/method/resnext) are replaced by the proposed SK convolutions, enabling the network to choose appropriate receptive field sizes in an adaptive manner. 

In SK units, there are three important hyper-parameters which determine the final settings of SK convolutions: the number of paths $M$ that determines the number of choices of different kernels to be aggregated, the group number $G$ that controls the cardinality of each path, and the reduction ratio $r$ that controls the number of parameters in the fuse operator. One typical setting of SK convolutions is $\text{SK}\left[M, G, r\right]$ to be $\text{SK}\left[2, 32, 16\right]$.

Source	Effective and Privacy preserving Tabular Data Synthesizing
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com