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What is InfoNCE loss?
The contrastive loss or InfoNCE loss in CPC, inspired by Noise Contrastive Estimation (NCE), uses cross-entropy loss to measure how well the model can classify the “future” representation amongst a set of unrelated “negative” samples.
What is noise contrastive estimation?
Noise Contrastive Estimation (NCE) is a powerful parameter estimation method for log-linear models, which avoids calculation of the partition function or its derivatives at each training step, a computationally demanding step in many cases. It is closely related to negative sampling methods, now widely used in NLP.
What is contrastive learning?
Contrastive learning is a framework that learns similar/dissimilar representations from data that are organized into similar/dissimilar pairs. This can be formulated as a dictionary look-up problem.
What is embedding loss?
Overview of Embedding Loss Layer The embedding loss functions include the contrastive loss function, the triplet loss function, and the quartet loss function. At a high level, the contrastive loss function learns discriminating features in image analysis by comparing image pairs.
Why does NCE loss work without understanding the math?
Honestly there is no intuitive way to understand why NCE loss will work without deeply understanding its math. To understand the math, you should read the original paper. The reason why NCE loss will work is because NCE approximates maximum likelihood estimation (MLE) when the ratio of noise to real data k increases.
What is the purpose of NS and NCE?
NCE is a method to learn parameters θ of a model p m ( x; θ) of a true data distribution p d ( x). So their purposes are different: NS learns to approximate a conditional label distribution p ( y | x), NCE approximates p d ( x).
Why does NCE loss work in TensorFlow implementation?
To understand the math, you should read the original paper. The reason why NCE loss will work is because NCE approximates maximum likelihood estimation (MLE) when the ratio of noise to real data k increases. The TensorFlow implementation works in practice.
Is the noise to data ratio in NCE wrong?
However, its implementation is conceptually wrong because the ratio of noise to real data k is different to the number of classes n sampled from noise distributions. TensorFlow NCE does not provide a variable for the noise to data ratio, and implicitly assumes n = k which I think is conceptually incorrect.