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 ## Description
 
-As part of the [Allen Institute for Cell Science's](allencell.org) mission to understand the principles by which human induced pluripotent stem cells establish and maintain robust dynamic localization of cellular structure, `CytoDL` aims to unify deep learning approaches for understanding 2D and 3D biological data as images, point clouds, and tabular data.
+As part of the [Allen Institute for Cell Science's](https://allencell.org) mission to understand the principles by which human induced pluripotent stem cells establish and maintain robust dynamic localization of cellular structure, `CytoDL` aims to unify deep learning approaches for understanding 2D and 3D biological data as images, point clouds, and tabular data.
 
 The bulk of `CytoDL`'s underlying structure bases the [lightning-hydra-template](https://github.com/ashleve/lightning-hydra-template) organization - we highly recommend that you familiarize yourself with their (short) docs for detailed instructions on running training, overrides, etc.
 
-Our currently available code is roughly split into two domains: image-to-image transformations and representation learning. The image-to-image code (denoted im2im) contains configuration files detailing how to train and predict using models for resolution enhancement using conditional GANs (e.g. predicting 100x images from 20x images), semantic and instance segmentation, and label-free prediction. We also provide configs for Masked Autoencoder (MAE) pretraining using a Vision Transformer (ViT) backbone and for training segmentation decoders from these pretrained features. Representation learning code includes a wide variety of Variational Auto Encoder (VAE) architectures. Due to dependency issues, equivariant autoencoders are not currently supported on Windows.
+Our currently available code is roughly split into two domains: image-to-image transformations and representation learning. The image-to-image code (denoted im2im) contains configuration files detailing how to train and predict using models for resolution enhancement using conditional GANs (e.g. predicting 100x images from 20x images), semantic and instance segmentation, and label-free prediction. We also provide configs for Masked Autoencoder (MAE) pretraining using a Vision Transformer (ViT) backbone and for training segmentation decoders from these pretrained features. Representation learning code includes a wide variety of Variational Auto Encoder (VAE) architectures and contrastive learning methods such as [VICReg](https://github.com/facebookresearch/vicreg). Due to dependency issues, equivariant autoencoders are not currently supported on Windows.
 
 As we rely on recent versions of pytorch, users wishing to train and run models on GPU hardware will need up-to-date NVIDIA drivers. Users with older GPUs should not expect code to work out of the box. Similarly, we do not currently support training/predicting on Mac GPUs. In most cases, cpu-based training should work when GPU training fails.