SimCLR model for image recognition
We have seen that SimCLR can do the following:
- Learn feature representations (unit hypersphere) by grouping similar images together and pushing dissimilar images apart.
- Balance alignment (keeping similar images together) and uniformity (preserving the maximum information).
- Learn on unlabeled training data.
The primary challenge is to use the unlabeled data (that comes from a similar but different distribution from the labeled data) to build a useful prior, which is then used to generate labels for the unlabeled set. Let's look at the architecture we will implement in this section.
Figure 8.7 – SimCLR architecture implementation
We will use the ResNet-50 as the Encoder, followed by a three-layer MLP as the projection head. We will then use logistic regression, or MLP, as the supervised classifier to measure the accuracy.
The SimCLR architecture involves the following steps, which we implement...
