Learn what cross-entropy loss is, how it works, and what are its advantages and disadvantages for classification tasks in neural networks. Skip to main content LinkedIn Articles ...

Addressing Imbalance in Multi-Label Classification Using Weighted Cross Entropy Loss Function Abstract: Training a model and network on an imbalanced dataset always has been a challenging problem in ...

I need to train a multi-label classifier for text topic classification task. Having searched around the internet, I follow the suggestion to use sigmoid + binary_crossentropy. But I can't get good ...

However, we are unsure about one aspect: when using 'cross_entropy' as the loss function, is there an automatic adjustment within the model considering that binary cross entropy typically expects the ...

Cross-entropy loss is a widely-used objective function for classification tasks, offering advantages such as robustness and compatibility with optimization algorithms like gradient descent.

In this study, we have benefited from weighted binary cross-entropy in the learning process as a loss function instead of ordinary cross-entropy (binary cross-entropy). This model allocates more ...