**VIME **, or **Value Imputation and Mask Estimation**, is a self- and semi-supervised learning framework for tabular data. It consists of a pretext task of estimating mask vectors from corrupted tabular data in addition to the reconstruction pretext task for self-supervised learning.

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VocGAN

VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network

VIME

VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain

A **CNN BiLSTM** is a hybrid bidirectional [LSTM](https://paperswithcode.com/method/lstm) and CNN architecture. In the original formulation applied to named entity recognition, it learns both character-level and word-level features. The CNN component is used to induce the character-level features. For each word the model employs a [convolution](https://paperswithcode.com/method/convolution) and a [max pooling](https://paperswithcode.com/method/max-pooling) layer to extract a new feature vector from the per-character feature vectors such as character embeddings and (optionally) character type.

Source	VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com