What is ReLIC?

ReLIC stands for Representation Learning via Invariant Causal Mechanisms, and is a type of self-supervised learning objective that allows for improved generalization guarantees. It does this by enforcing invariant prediction of proxy targets across augmentations through an invariance regularizer.

How Does ReLIC Work?

ReLIC works by using a proxy task loss and Kullback-Leibler (KL) divergence to calculate similarity scores. Concretely, it associates every datapoint with a label and uses pairs of points to compute similarity scores using pairs of augmentations to perform a style intervention. To compare representations, a fully-connected neural network called the critic is used.

The objective of ReLIC is to learn representations by minimizing the following objective over the full set of data and augmentations:

$$ -\sum_{i=1}^{N} \sum\_{a\_{l k}} \log \frac{\exp \left(\phi\left(f\left(x\_{i}^{a_{l}}\right), h\left(x\_{i}^{a\_{k}}\right)\right) / \tau\right)}{\sum\_{m=1}^{M} \exp \left(\phi\left(f\left(x\_{i}^{a\_{l}}\right), h\left(x\_{m}^{a\_{k}}\right)\right) / \tau\right)}+\alpha \sum\_{a\_{l k}, a\_{q t}} K L\left(p^{d o\left(a\_{l k}\right)}, p^{d o\left(a\_{q t}\right)}\right) $$

With M being the number of points used to construct the contrast set and α being the weighting of the invariance penalty.

What is the Purpose of ReLIC?

The purpose of ReLIC is to improve the generalization guarantees of self-supervised learning objectives. By enforcing invariant prediction of proxy targets across augmentations through an invariance regularizer, ReLIC allows for improved generalization guarantees that can be useful in a variety of applications.

How is ReLIC Used in Machine Learning?

ReLIC is used in machine learning to improve the generalization guarantees of self-supervised learning objectives. It does this by enforcing invariant prediction of proxy targets across augmentations through an invariance regularizer which yields improved generalization guarantees.

ReLIC is particularly useful in applications where accurate and reliable predictions are necessary. For example, ReLIC can be used in medical diagnosis to ensure that predictions are invariant across different patients and settings.

ReLIC is a self-supervised learning objective that improves the generalization guarantees of machine learning models. By enforcing invariant prediction of proxy targets across augmentations through an invariance regularizer, ReLIC can be useful in a variety of applications where accurate and reliable predictions are necessary.

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