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Found 14 papers, 4 papers with code
Embodied Adversarial Attack: A Dynamic Robust Physical Attack in Autonomous Driving
As physical adversarial attacks become extensively applied in unearthing the potential risk of security-critical scenarios, especially in autonomous driving, their vulnerability to environmental changes has also been brought to light.
DeepMetricEye: Metric Depth Estimation in Periocular VR Imagery
Despite the enhanced realism and immersion provided by VR headsets, users frequently encounter adverse effects such as digital eye strain (DES), dry eye, and potential long-term visual impairment due to excessive eye stimulation from VR displays and pressure from the mask.
Unified Adversarial Patch for Visible-Infrared Cross-modal Attacks in the Physical World
We also demonstrate the effectiveness of our approach in physical-world scenarios under various settings, including different angles, distances, postures, and scenes for both visible and infrared sensors.
Unified Adversarial Patch for Cross-modal Attacks in the Physical World
To show the potential risks under such scenes, we propose a unified adversarial patch to perform cross-modal physical attacks, i. e., fooling visible and infrared object detectors at the same time via a single patch.
Out-of-Distribution Detection with Class Ratio Estimation
In this work, we propose to unify density ratio based methods under a novel framework that builds energy-based models and employs differing base distributions.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Spread Flows for Manifold Modelling
Flow-based models typically define a latent space with dimensionality identical to the observational space.
Layer-Parallel Training of Residual Networks with Auxiliary Variables
Backpropagation algorithm is indispensable for training modern residual networks (ResNets) and usually tends to be time-consuming due to its inherent algorithmic lockings.
On the Latent Space of Flow-based Models
Flow-based generative models typically define a latent space with dimensionality identical to the observational space.
Self-supervised Disentangled Representation Learning
In our experiments, we find that even the data is only augmented along a few latent variables, more latent variables can be identified, and adding a small noise in data space can stabilize this outcome.
Identifying Informative Latent Variables Learned by GIN via Mutual Information
However, in this paper, we point out that the method taken by GIN for informative latent variables identification is not theoretically supported and can be disproved by experiments.
A Practical Layer-Parallel Training Algorithm for Residual Networks
Gradient-based algorithms for training ResNets typically require a forward pass of the input data, followed by back-propagating the objective gradient to update parameters, which are time-consuming for deep ResNets.
Towards Better Understanding of Disentangled Representations via Mutual Information
Most existing works on disentangled representation learning are solely built upon an marginal independence assumption: all factors in disentangled representations should be statistically independent.
On the Approximation Properties of Random ReLU Features
We study the approximation properties of random ReLU features through their reproducing kernel Hilbert space (RKHS).
But How Does It Work in Theory? Linear SVM with Random Features
We prove that, under low noise assumptions, the support vector machine with $N\ll m$ random features (RFSVM) can achieve the learning rate faster than $O(1/\sqrt{m})$ on a training set with $m$ samples when an optimized feature map is used.
