Conditional Flow Matching (CFM) is a fast way to train continuous normalizing flow (CNF) models. CFM is a simulation-free training objective for continuous normalizing flows that allows conditional generative modelling and speeds up training and inference.

**MoCo v2** is an improved version of the [Momentum Contrast](https://paperswithcode.com/method/moco) self-supervised learning algorithm. Motivated by the findings presented in the [SimCLR](https://paperswithcode.com/method/simclr) paper, authors:

- Replace the 1-layer fully connected layer with a 2-layer MLP head with [ReLU](https://paperswithcode.com/method/relu) for the unsupervised training stage.
- Include blur augmentation.
- Use cosine learning rate schedule.

These modifications enable MoCo to outperform the state-of-the-art SimCLR with a smaller batch size and fewer epochs.

MoCo v2

Improved Baselines with Momentum Contrastive Learning

Flow Matching

Flow Matching for Generative Modeling

Per the authors, Graph Isomorphism Network (GIN) generalizes the WL test and hence achieves maximum discriminative power among GNNs.

Source	Flow Matching for Generative Modeling
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com