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When to use joint loss with regularization terms?
It is frequent to add some regularization terms to the cost function A joint loss is a sum of two losses : and in the case of multi-modal classification, where data is composed of multiple parts, such as for example images (x1) and texts (x2), we usually use the joint loss with multiple embeddings, which are high dimensional feature spaces :
Can a loss function be a Gaussian distribution?
Of course, this is equivalent to a multi-dimensional Gaussian distribution with uniform variance. Also other Lp norms are possible as loss functions. Image under CC BY 4.0 from the Deep Learning Lecture. Okay, so well there’s not just L2-losses.
Is the gradient of the hinge loss differentiable?
The hinge loss is a convex function, easy to minimize. Although it is not differentiable, it’s easy to compute its gradient locally. There exists also a smooth version of the gradient. It is simply the square of the hinge loss :
How is joint latent similarity used in Zero Shot Learning?
In Zero shot learning via joint latent similarity Embedding, Zhang et al. propose an algorithm that iteratively assigns to each example in the dataset an embedding value that maximizes the objective function over all data, then optimizes for this assignment at a very good computational cost.
How is regularization loss modulated in TensorFlow?
A way to obtain this is to add a regularization term to the loss function. This term is a generic function, which modifies the “global” loss (as in, the sum of the network loss and the regularization loss) in order to drive the optimization algorithm in desired directions.
How is the square loss function used in regularized least squares?
For regularized least squares the square loss function is introduced: , this approach may overfit the training data, and lead to poor generalization. Thus, it should somehow constrain or penalize the complexity of the function . In RLS, this is accomplished by choosing functions from a reproducing kernel Hilbert space (RKHS) .
How is a regularization term related to the optimization function?
The regularization term, or penalty, imposes a cost on the optimization function to make the optimal solution unique. Independent of the problem or model, there is always a data term, that corresponds to a likelihood of the measurement and a regularization term that corresponds to a prior.