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In this paper, we proposed a two-dimensional distance-based self-attention regularization method with a newly introduced distance loss to address vision transformers lack of inductive biases. The distance loss uses the Manhattan distance between image patches to penalize self-attention computation between them.

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Apr 19, 2018 · Dropout. This is the one of the most interesting types of regularization techniques. It also produces very good results and is consequently the most frequently used regularization technique in the field of deep learning. To understand dropout, let’s say our neural network structure is akin to the one shown below:.

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Jul 17, 2020 · The first regularization technique we’ll be looking at is the L1 and L2 regularization.L1 and L2 Regularization.There are two popular regularization parameters: L1 and L2.L1 is called “Lasso”, and L2 is called “Ridge”. Moreover, L1 could also indicate the sum of the absolute weights, and L2 could indicate the sum of the squared.

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