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Found 11 papers, 2 papers with code
LocalStyleFool: Regional Video Style Transfer Attack Using Segment Anything Model
Benefiting from the popularity and scalably usability of Segment Anything Model (SAM), we first extract different regions according to semantic information and then track them through the video stream to maintain the temporal consistency.
3D Face Reconstruction Using A Spectral-Based Graph Convolution Encoder
To overcome this limitation and enhance the reconstruction of 3D structural features, we propose an innovative approach that integrates existing 2D features with 3D features to guide the model learning process.
Unsupervised multiple choices question answering via universal corpus
Unsupervised question answering is a promising yet challenging task, which alleviates the burden of building large-scale annotated data in a new domain.
Influential Recommender System
Traditional recommender systems are typically passive in that they try to adapt their recommendations to the user's historical interests.
What's the relationship between CNNs and communication systems?
The interpretability of Convolutional Neural Networks (CNNs) is an important topic in the field of computer vision.
NN-PARS: A Parallelized Neural Network Based Circuit Simulation Framework
The shrinking of transistor geometries as well as the increasing complexity of integrated circuits, significantly aggravate nonlinear design behavior.
The Nonequilibrium Mechanism of Noise Enhancer synergizing with Activator in HIV Latency Reactivation
Noise-modulating chemicals can synergize with transcriptional activators in reactivating latent HIV to eliminate latent HIV reservoirs.
Defending from adversarial examples with a two-stream architecture
We demonstrate that our two-stream architecture is robust to adversarial examples built by currently known attacking algorithms.
Fictitious GAN: Training GANs with Historical Models
Inspired by the fictitious play learning process, a novel training method, referred to as Fictitious GAN, is introduced.
Training Generative Adversarial Networks via Primal-Dual Subgradient Methods: A Lagrangian Perspective on GAN
This formulation shows the connection between the standard GAN training process and the primal-dual subgradient methods for convex optimization.
A Parameter-Free Learning Automaton Scheme
For a learning automaton, a proper configuration of its learning parameters, which are crucial for the automaton's performance, is relatively difficult due to the necessity of a manual parameter tuning before real applications.
