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Found 5 papers, 1 papers with code
Parameter Efficient Audio Captioning With Faithful Guidance Using Audio-text Shared Latent Representation
Then, we propose a parameter efficient inference time faithful decoding algorithm that enables smaller audio captioning models with performance equivalent to larger models trained with more data.
Mitigating Gradient-based Adversarial Attacks via Denoising and Compression
To reduce the training time of the defense for a small trade-off in performance, we propose the hidden layer defense, which involves feeding the output of the encoder of a denoising autoencoder into the network.
Ensemble Noise Simulation to Handle Uncertainty about Gradient-based Adversarial Attacks
Gradient-based adversarial attacks on neural networks can be crafted in a variety of ways by varying either how the attack algorithm relies on the gradient, the network architecture used for crafting the attack, or both.
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks
The reliance on deep learning algorithms has grown significantly in recent years.
Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder Approach
Machine Learning models are vulnerable to adversarial attacks that rely on perturbing the input data.
