The article introduces a novel loss function called Large-Margin Softmax (L-Softmax) for Convolutional Neural Networks (CNNs). Traditional softmax loss, commonly used in CNNs, does not explicitly encourage discriminative feature learning. L-Softmax is designed to enhance intra-class compactness and inter-class separability by introducing a margin parameter $m$, which controls the angular margin between classes. This loss function is optimized using stochastic gradient descent and has been shown to significantly improve performance on various visual classification and verification tasks. The paper explains that the softmax loss can be generalized to L-Softmax by modifying the cosine similarity calculation to incorporate the margin parameter $m$. This adjustment leads to a more rigorous decision boundary, resulting in more discriminative features. The L-Softmax loss is also shown to help prevent overfitting by making the learning task more challenging. The authors validate their approach through extensive experiments on four benchmark datasets, including MNIST, CIFAR10, CIFAR100, and LFW. Results demonstrate that L-Softmax outperforms traditional softmax loss and other state-of-the-art methods in terms of classification accuracy and verification performance. The paper also provides a geometric interpretation of the L-Softmax loss, showing how it enhances the angular margin between classes. This interpretation helps in understanding the effectiveness of the loss function in promoting discriminative feature learning. The authors further discuss the flexibility of the L-Softmax loss, which allows for adjustable difficulty in learning tasks, and its compatibility with various CNN architectures and training strategies. Overall, the L-Softmax loss is shown to be a powerful tool for improving the performance of CNNs in visual recognition tasks.The article introduces a novel loss function called Large-Margin Softmax (L-Softmax) for Convolutional Neural Networks (CNNs). Traditional softmax loss, commonly used in CNNs, does not explicitly encourage discriminative feature learning. L-Softmax is designed to enhance intra-class compactness and inter-class separability by introducing a margin parameter $m$, which controls the angular margin between classes. This loss function is optimized using stochastic gradient descent and has been shown to significantly improve performance on various visual classification and verification tasks. The paper explains that the softmax loss can be generalized to L-Softmax by modifying the cosine similarity calculation to incorporate the margin parameter $m$. This adjustment leads to a more rigorous decision boundary, resulting in more discriminative features. The L-Softmax loss is also shown to help prevent overfitting by making the learning task more challenging. The authors validate their approach through extensive experiments on four benchmark datasets, including MNIST, CIFAR10, CIFAR100, and LFW. Results demonstrate that L-Softmax outperforms traditional softmax loss and other state-of-the-art methods in terms of classification accuracy and verification performance. The paper also provides a geometric interpretation of the L-Softmax loss, showing how it enhances the angular margin between classes. This interpretation helps in understanding the effectiveness of the loss function in promoting discriminative feature learning. The authors further discuss the flexibility of the L-Softmax loss, which allows for adjustable difficulty in learning tasks, and its compatibility with various CNN architectures and training strategies. Overall, the L-Softmax loss is shown to be a powerful tool for improving the performance of CNNs in visual recognition tasks.