Search Results for author:
Found 5 papers, 2 papers with code
SwitchLight: Co-design of Physics-driven Architecture and Pre-training Framework for Human Portrait Relighting
We introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pre-training framework.
MHSAN: Multi-Head Self-Attention Network for Visual Semantic Embedding
Thus, in addition to the joint embedding space, we propose a novel multi-head self-attention network to capture various components of visual and textual data by attending to important parts in data.
Reducing Domain Gap by Reducing Style Bias
Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift.
Ranked #62 on
Domain Generalization
on PACS
Representation of White- and Black-Box Adversarial Examples in Deep Neural Networks and Humans: A Functional Magnetic Resonance Imaging Study
However, DNNs exhibit idiosyncrasies that suggest their visual representation and processing might be substantially different from human vision.
Stochastic Quantized Activation: To prevent Overfitting in Fast Adversarial Training
Existing neural networks are vulnerable to "adversarial examples"---created by adding maliciously designed small perturbations in inputs to induce a misclassification by the networks.
