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Found 6 papers, 2 papers with code
Steal Now and Attack Later: Evaluating Robustness of Object Detection against Black-box Adversarial Attacks
Latency attacks against object detection represent a variant of adversarial attacks that aim to inflate the inference time by generating additional ghost objects in a target image.
Overload: Latency Attacks on Object Detection for Edge Devices
Nowadays, the deployment of deep learning-based applications is an essential task owing to the increasing demands on intelligent services.
Low-rank Tensor Decomposition for Compression of Convolutional Neural Networks Using Funnel Regularization
Tensor decomposition is one of the fundamental technique for model compression of deep convolution neural networks owing to its ability to reveal the latent relations among complex structures.
LTD: Low Temperature Distillation for Robust Adversarial Training
Despite the popularity of neural network models, a significant gap exists between the natural and robust accuracy of these models.
Knowledge Distillation with Feature Maps for Image Classification
One promising method is knowledge distillation (KD), which creates a fast-to-execute student model to mimic a large teacher network.
Escaping from Collapsing Modes in a Constrained Space
Generative adversarial networks (GANs) often suffer from unpredictable mode-collapsing during training.
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Image Generation
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