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The loss incurred on this batch is the multi-class N-pair loss, [12] which is a symmetric cross-entropy loss over similarity scores: / / / / In essence, this loss function encourages the dot product between matching image and text vectors to be high, while discouraging high dot products between non-matching pairs.
The loss function is defined using triplets of training points of the form (,,).In each triplet, (called an "anchor point") denotes a reference point of a particular identity, (called a "positive point") denotes another point of the same identity in point , and (called a "negative point") denotes an point of an identity different from the identity in point and .
Loss functions are implemented as sub-classes of Criterion, which has a similar interface to Module. It also has forward() and backward() methods for computing the loss and backpropagating gradients, respectively. Criteria are helpful to train neural network on classical tasks.
Learning in twin networks can be done with triplet loss or contrastive loss. For learning by triplet loss a baseline vector (anchor image) is compared against a positive vector (truthy image) and a negative vector (falsy image). The negative vector will force learning in the network, while the positive vector will act like a regularizer.
In many applications, objective functions, including loss functions as a particular case, are determined by the problem formulation. In other situations, the decision maker’s preference must be elicited and represented by a scalar-valued function (called also utility function) in a form suitable for optimization — the problem that Ragnar Frisch has highlighted in his Nobel Prize lecture. [4]
Given the binary nature of classification, a natural selection for a loss function (assuming equal cost for false positives and false negatives) would be the 0-1 loss function (0–1 indicator function), which takes the value of 0 if the predicted classification equals that of the true class or a 1 if the predicted classification does not match ...
The loss function is a function that maps values of one or more variables onto a real number intuitively representing some "cost" associated with those values. For backpropagation, the loss function calculates the difference between the network output and its expected output, after a training example has propagated through the network.
The loss function used in DINO is the cross-entropy loss between the output of the teacher network (′) and the output of the student network (). The teacher network is an exponentially decaying average of the student network's past parameters: θ t ′ = α θ t + α ( 1 − α ) θ t − 1 + ⋯ {\displaystyle \theta '_{t}=\alpha \theta _{t ...