Shypd.aiThe largest open index of AI toolsmean-teacher is an open-source semi-supervised learning method that improves image recognition accuracy. It uses a student-teacher model approach with exponential moving average for the teacher's weights.
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mean-teacher is a state-of-the-art semi-supervised learning method designed to enhance image recognition capabilities, particularly when labeled data is scarce. The approach involves a 'student' model and a 'teacher' model. Both models process the same minibatch of inputs, but with separate random augmentations or noise. The student's weights are updated normally by an optimizer, while the teacher's weights are maintained as an exponential moving average of the student's weights. This unique mechanism, where the teacher's parameters are a smoothed version of the student's, is the core contribution of the Mean Teacher method. It has been shown to improve state-of-the-art results on datasets like ImageNet and CIFAR-10, working effectively with modern architectures such as ResNets. Implementations are available for both TensorFlow and PyTorch, with the PyTorch version being more adaptable.
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