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.

Why NCE loss?

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. Where Pn(w) is the noise distribution.

What is InfoNCE loss?

InfoNCE loss is a widely used loss function for contrastive model training. It aims to estimate the mutual information between a pair of variables by discriminating between each positive pair and its associated K negative pairs.

What is contrastive loss?

Contrastive loss takes the output of the network for a positive example and calculates its distance to an example of the same class and contrasts that with the distance to negative examples.

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 negative sampling in word2vec?

Subsampling frequent words to decrease the number of training examples. Modifying the optimization objective with a technique they called “Negative Sampling”, which causes each training sample to update only a small percentage of the model’s weights.

What is hierarchical Softmax?

Hierarchical softmax is an alternative to the softmax in which the probability of any one outcome depends on a number of model parameters that is only logarithmic in the total number of outcomes. In “vanilla” softmax, on the other hand, the number of such parameters is linear in the number of total number of outcomes.

How does contrastive learning work?

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. The contrastive loss can be minimized by various mechanisms that differ in how the keys are maintained.

Is contrastive learning unsupervised?

Contrastive learning can be applied to both supervised and unsupervised data and has been shown to achieve good performance on a variety of vision and language tasks.

What is the meaning of contrastive?

tending to contrast; contrasting. contrastive colors. studying or exhibiting the congruences and differences between two languages or dialects without reference to their origins: contrastive linguistics.

What is contrastive method?

Contrastive analysis is the systematic study of a pair of languages with a view to identifying their structural differences and similarities. Historically it has been used to establish language genealogies.

What is Skip-gram in NLP?

Skip-gram is one of the unsupervised learning techniques used to find the most related words for a given word. Skip-gram is used to predict the context word for a given target word. It’s reverse of CBOW algorithm. Here, target word is input while context words are output.

What is noise contrastive estimation in NCE?

Noise Contrastive Estimation. Basically this is selecting a sample from the true distribution which consist of the true class and some other nosy class labels . Then taking the softmax over it . In NCE , We try to get rid of the expensive cost function.

What is NoNOISE contrastive estimation?

Noise contrastive estimation reduces the complexity of optimization by replacing the multi-class classification to binary classification and using sampling from noise distributions. Concretely, we introduce noise distribution P n ( w). This noise distribution could be context-dependent or context independent.

How do you use K noise samples in NCE?

Sampling K noise sample words from the unigram distribution and substituting it to the second part of the equation when training . The sum over k noise samples instead of a sum over the entire vocabulary, making the NCE training time linear in the number of noise samples and independent of the vocabulary size.

What is the difference between NCE and negative sampling?

The primary difference in implementation between NCE and Negative Sampling is that in NCE, the probability that a sample came from the noise distribution is explicitly accounted for, and the problem is cast as a formal estimate of the log-odds ratio that a particular sample came from the real data distribution instead of the noise distribution.