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Partially Recentralization Softmax Loss for Vision-Language Models Robustness
However, it has been shown that multimodal NLP are vulnerable to adversarial attacks, where the outputs of a model can be dramatically changed by a perturbation to the input.
Distribution-restrained Softmax Loss for the Model Robustness
Recently, the robustness of deep learning models has received widespread attention, and various methods for improving model robustness have been proposed, including adversarial training, model architecture modification, design of loss functions, certified defenses, and so on.
Towards interpreting computer vision based on transformation invariant optimization
Interpreting how does deep neural networks (DNNs) make predictions is a vital field in artificial intelligence, which hinders wide applications of DNNs.
Genetic Algorithm based hyper-parameters optimization for transfer Convolutional Neural Network
Decision of transfer layers and trainable layers is a major task for design of the transfer convolutional neural networks (CNN).
Generalization Study of Quantum Neural Network
In this model, we mapped the feature data to a quantum state in Hilbert space firstly, and then implement unitary evolution on it, in the end, we can get the classification result by im-plement measurement on the quantum state.
