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An In-Depth Guide to Contrastive Learning: Techniques, Models, and Applications
Discover the fundamentals of contrastive learning, including key techniques like SimCLR, MoCo, and CLIP. Learn how contrastive learning improves unsupervised learning and its practical applications.
In the end, if we fine-tune the CNN on some labelled images, it helps increase its performance and generalization on diverse other (downstream) tasks. Not only did SimCLR introduce a new model with very good performance (you can check its paper for a detailed results analysis), but its authors also gave some new insights which can be useful for almost any contrastive learning method. MoCo class’s constructor initializes the attributes like K, m and T. As we can see, it uses the default values of feature dimension ( dim) as 128, queue size ( K) as 16-bits (65,536), while momentum co-efficient,μ is 0.999 (quite slow moving average).
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