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DropConnect is a regularization technique used in deep learning for neural networks. It was first introduced by Wan et al. in their paper "Regularization of Neural Networks using DropConnect". DropConnect is similar to Dropout, another regularization technique, but instead of randomly setting individual neuron outputs to zero, it randomly sets weights connecting neurons to zero during training. This means that some connections between neurons are dropped or deactivated temporarily.During training, each connection between two neurons has a probability p of being dropped or set to zero. The value of p is usually chosen as a hyperparameter and can be adjusted based on the network architecture and dataset.By dropping connections between neurons, DropConnect prevents the network from overfitting and reduces the reliance on individual units or features. It encourages the network to learn more robust and generalizable features by forcing different subnetworks to learn different parts of the data.During inference or evaluation, all weights are used without any dropout or dropping of connections. This means that the full network can be executed for prediction without any stochasticity introduced by dropout-like techniques.DropConnect has been shown to improve generalization performance on various datasets and architectures, including convolutional neural networks (CNNs) for image classification tasks. However, it may require additional training time since more parameters are involved.Overall, DropConnect provides a powerful regularization technique that can help prevent overfitting and improve generalization in deep neural networks.

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