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Overview of the method is here: Google AI Blogpost
Also, explore the interactive visualization that demonstrates the practical properties of the Bi-Tempered logistic loss.
Bi-Tempered logistic loss is a generalized softmax cross-entropy loss function with bounded loss value per sample and a heavy-tail softmax probability function.
Bi-tempered loss generalizes (with a bias correction term):
- Zhang & Sabuncu. "Generalized cross entropy loss for training deep neural networks with noisy labels." In NeurIPS 2018.
which is recovered when 0.0 <= t1 <= 1.0 and t2 = 1.0. It also includes:
- Ding & Vishwanathan. "t-Logistic regression." In NeurIPS 2010.
for t1 = 1.0 and t2 >= 1.0.
Bi-tempered loss is equal to the softmax cross entropy loss when t1 = t2 = 1.0. For 0.0 <= t1 < 1.0 and t2 > 1.0, bi-tempered loss provides a more robust alternative to the cross entropy loss for handling label noise and outliers.
A replacement for standard logistic loss function: tf.losses.softmax_cross_entropy is available here
def bi_tempered_logistic_loss(activations,
labels,
t1,
t2,
label_smoothing=0.0,
num_iters=5):
"""Bi-Tempered Logistic Loss with custom gradient.
Args:
activations: A multi-dimensional tensor with last dimension `num_classes`.
labels: A tensor with shape and dtype as activations.
t1: Temperature 1 (< 1.0 for boundedness).
t2: Temperature 2 (> 1.0 for tail heaviness, < 1.0 for finite support).
label_smoothing: Label smoothing parameter between [0, 1).
num_iters: Number of iterations to run the method.
Returns:
A loss tensor.
"""Replacements are also available for the transfer functions:
Tempered version of tf.nn.sigmoid and jax.nn.sigmoid:
def tempered_sigmoid(activations, t, num_iters=5):
"""Tempered sigmoid function.
Args:
activations: Activations for the positive class for binary classification.
t: Temperature > 0.0.
num_iters: Number of iterations to run the method.
Returns:
A probabilities tensor.
"""Tempered version of tf.nn.softmax and jax.nn.softmax:
def tempered_softmax(activations, t, num_iters=5):
"""Tempered softmax function.
Args:
activations: A multi-dimensional tensor with last dimension `num_classes`.
t: Temperature > 0.0.
num_iters: Number of iterations to run the method.
Returns:
A probabilities tensor.
"""When referencing Bi-Tempered loss, cite this paper:
@inproceedings{amid2019robust,
title={Robust bi-tempered logistic loss based on bregman divergences},
author={Amid, Ehsan and Warmuth, Manfred KK and Anil, Rohan and Koren, Tomer},
booktitle={Advances in Neural Information Processing Systems},
pages={15013--15022},
year={2019}
}
We are eager to collaborate with you too! Please send us a pull request or open a github issue. Please see this doc for further details
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