Deep Mutual Learning (DML) is a novel approach to model compression and training, where multiple student networks learn collaboratively and teach each other during training, rather than relying on a single pre-trained teacher network. Unlike traditional model distillation, which transfers knowledge from a powerful teacher to a smaller student, DML enables students to learn from each other, improving performance without the need for a strong teacher. The method involves training multiple networks simultaneously, with each network learning to match the probability estimates of its peers. This collaborative learning process enhances generalization and robustness, as demonstrated through experiments on benchmark datasets such as CIFAR-100 and Market-1501. The DML approach is formulated with a loss function that combines a conventional supervised learning loss with a KL divergence-based mimicry loss, encouraging students to align their predictions with those of their peers. This dual loss structure allows each network to learn both the true labels and the probability distributions of other networks, leading to better performance. The optimization process is iterative, with each network updating its parameters based on the predictions of the others. Experiments show that DML outperforms traditional distillation methods, even when using smaller networks. The method is effective across various network architectures and can be extended to larger student cohorts. Additionally, DML achieves better results than conventional distillation, even when using a single pre-trained teacher. The approach also benefits from ensemble learning, where the predictions of multiple networks are combined to improve performance. The success of DML is attributed to its ability to find robust solutions with high posterior entropy, which enhances generalization. By encouraging networks to learn from each other, DML reduces the risk of groupthink and improves the overall performance of the model. The method is applicable to a wide range of tasks, including classification and person re-identification, and can be used to improve both small and large networks. Overall, DML provides a simple yet effective way to enhance the performance of deep neural networks through collaborative learning.Deep Mutual Learning (DML) is a novel approach to model compression and training, where multiple student networks learn collaboratively and teach each other during training, rather than relying on a single pre-trained teacher network. Unlike traditional model distillation, which transfers knowledge from a powerful teacher to a smaller student, DML enables students to learn from each other, improving performance without the need for a strong teacher. The method involves training multiple networks simultaneously, with each network learning to match the probability estimates of its peers. This collaborative learning process enhances generalization and robustness, as demonstrated through experiments on benchmark datasets such as CIFAR-100 and Market-1501. The DML approach is formulated with a loss function that combines a conventional supervised learning loss with a KL divergence-based mimicry loss, encouraging students to align their predictions with those of their peers. This dual loss structure allows each network to learn both the true labels and the probability distributions of other networks, leading to better performance. The optimization process is iterative, with each network updating its parameters based on the predictions of the others. Experiments show that DML outperforms traditional distillation methods, even when using smaller networks. The method is effective across various network architectures and can be extended to larger student cohorts. Additionally, DML achieves better results than conventional distillation, even when using a single pre-trained teacher. The approach also benefits from ensemble learning, where the predictions of multiple networks are combined to improve performance. The success of DML is attributed to its ability to find robust solutions with high posterior entropy, which enhances generalization. By encouraging networks to learn from each other, DML reduces the risk of groupthink and improves the overall performance of the model. The method is applicable to a wide range of tasks, including classification and person re-identification, and can be used to improve both small and large networks. Overall, DML provides a simple yet effective way to enhance the performance of deep neural networks through collaborative learning.