Keyword Analysis & Research: label smoothing

towardsdatascience.com

https://towardsdatascience.com/what-is-label-smoothing-108debd7ef06

A technique to make your model less overconfident Overconfidence and Calibration. A classification model is calibrated if its predicted probabilities of outcomes reflect... Formula of Label Smoothing. If α = 0, we obtain the original one-hot encoded y_hot. If α = 1, we get the uniform... Motivation of Label Smoothing. Label smoothing is used when the loss function is...

Overconfidence and Calibration. A classification model is calibrated if its predicted probabilities of outcomes reflect...

Formula of Label Smoothing. If α = 0, we obtain the original one-hot encoded y_hot. If α = 1, we get the uniform...

Motivation of Label Smoothing. Label smoothing is used when the loss function is...

DA: 80 PA: 11 MOZ Rank: 47

towardsdatascience.com

https://towardsdatascience.com/what-is-label-smoothing-108debd7ef06

A technique to make your model less overconfident Overconfidence and Calibration. A classification model is calibrated if its predicted probabilities of outcomes reflect... Formula of Label Smoothing. If α = 0, we obtain the original one-hot encoded y_hot. If α = 1, we get the uniform... Motivation ...

DA: 43 PA: 22 MOZ Rank: 86

paperswithcode.com

https://paperswithcode.com/method/label-smoothing

Label Smoothing. Label Smoothing is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the likelihood of log. p ( y ∣ x) directly can be harmful. …

DA: 53 PA: 79 MOZ Rank: 21

towardsdatascience.com

https://towardsdatascience.com/label-smoothing-make-your-model-less-over-confident-b12ea6f81a9a

Jun 03, 2021 · Label smoothing is a form of output distribution regularization that prevents overfitting of a neural network by softening the ground-truth labels in the training data in an attempt to penalize overconfident outputs. The intuition …

DA: 48 PA: 40 MOZ Rank: 81

rickwierenga.com

https://rickwierenga.com/blog/machine%20learning/LabelSmoothing.html

Aug 12, 2019 · Label smoothing is a mathematical technique that helps machine learning models to deal with data where some labels are wrong. The problem with the approach Cross entropy loss for binary classification A commonly used loss function for logistic regression is cross-entropy loss. For binary classification problems ( m = 2) it is defined as follows:

DA: 55 PA: 80 MOZ Rank: 20

quickml.info

https://www.quickml.info/post/what-is-label-smoothing

Mar 11, 2021 · Label Smoothing is one of the many regularization techniques Formula of Label Smoothing -> y_ls = (1 - a) * y_hot + a / k k -> number of classes a -> hyper-parameter which controls the extent of label smoothing a - 0 (original distribution) ; a - 1 (uniform distribution)

DA: 28 PA: 5 MOZ Rank: 3

medium.com

https://lessw.medium.com/label-smoothing-deep-learning-google-brain-explains-why-it-works-and-when-to-use-sota-tips-977733ef020

Dec 08, 2019 · Label smoothing is a loss function modification that has been shown to be very effective for training deep learning networks. Label smoothing improves accuracy in image classification, translation,...

DA: 44 PA: 82 MOZ Rank: 79

arxiv.org

https://arxiv.org/abs/1906.02629

Jun 06, 2019 · Abstract: The generalization and learning speed of a multi-class neural network can often be significantly improved by using soft targets that are a weighted average of the hard targets and the uniform distribution over labels. Smoothing the labels in this way prevents the network from becoming over-confident and label smoothing has been used in many state-of …

DA: 27 PA: 7 MOZ Rank: 90

zhihu.com

https://zhuanlan.zhihu.com/p/116466239

def smoothing_cross_entropy (logits,labels,vocab_size,confidence): with tf.name_scope ("smoothing_cross_entropy", values= [logits, labels]): # Low confidence is given to all non-true labels, uniformly. low_confidence = (1.0 - confidence) / to_float (vocab_size - 1) # Normalizing constant is the best cross-entropy value with soft targets.

DA: 53 PA: 56 MOZ Rank: 83