Balanced Softmax

Balanced Softmax is a loss function modification designed to address class imbalance problems in deep learning, improving model performance on datasets with skewed class distributions. Current research focuses on its application within various contexts, including semi-supervised learning, adversarial training, and contrastive learning, often combined with techniques like data augmentation or other loss function adjustments to further enhance robustness and accuracy. This approach is significant because it tackles a pervasive issue in real-world datasets, leading to more reliable and generalizable models across diverse applications, particularly in areas like medical image analysis and object recognition.