# Contrastive Model: Instance-Instance

It was discovered that the success of
[[mutual information based contrastive learning]]
Contrastive Model: Context-Instance
In contrastive methods, we can manipulate the data to create data entries and infer the changes using a model. These methods are models that “predict relative position”1. Common tricks are
shuffling image sections like jigsaw, and rotate the image. We can also adjust the model to discriminate the similarities and differences. For example, to generate contrast, we can also use [[Mutual Information]] Mutual Information Mutual information is defined as $$ I(X;Y) = \mathbb E_{p_{XY}} \ln …
is more related to the encoder architecture and the negative sampling strategy^{1}. Instance-instance method is more direct in solving the contrastive problem. It take the instance itself directly and make comparisons for discrimination.

## Cluster Discrimination

## Instance Discrimination

There are two interesting models under the umbrella of instance discrimination,

- MoCo, and
- SimCLR.

L Ma (2021). 'Contrastive Model: Instance-Instance', Datumorphism, 08 April. Available at: https://datumorphism.leima.is/wiki/machine-learning/contrastive-models/instance-instance/.