Search Results for author:
Found 9 papers, 3 papers with code
Class Incremental Learning for Adversarial Robustness
We observe that combining incremental learning with naive adversarial training easily leads to a loss of robustness.
Indirect Gradient Matching for Adversarial Robust Distillation
In contrast to these approaches, we aim to transfer another piece of knowledge from the teacher, the input gradient.
Introducing Competition to Boost the Transferability of Targeted Adversarial Examples through Clean Feature Mixup
Deep neural networks are widely known to be susceptible to adversarial examples, which can cause incorrect predictions through subtle input modifications.
RainUNet for Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts
This paper presents a solution to the Weather4cast 2022 Challenge Stage 2.
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input
To tackle this limitation, we propose the object-based diverse input (ODI) method that draws an adversarial image on a 3D object and induces the rendered image to be classified as the target class.
Balancing Domain Experts for Long-Tailed Camera-Trap Recognition
Here, we propose a unified framework and introduce two datasets for long-tailed camera-trap recognition.
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification
Adversarial attack is aimed at fooling the target classifier with imperceptible perturbation.
Applying Tensor Decomposition to image for Robustness against Adversarial Attack
However, it turns out a deep learning based model is highly vulnerable to some small perturbation called an adversarial attack.
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation
Nowadays, Deep learning techniques show dramatic performance on computer vision area, and they even outperform human.
