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Found 13 papers, 2 papers with code
Hyperbolic Contrastive Learning for Hierarchical 3D Point Cloud Embedding
Hyperbolic spaces allow for more efficient modeling of complex, hierarchical structures, which is particularly beneficial in tasks involving multi-modal data.
Face Forgery Detection with Elaborate Backbone
Specifically, we analyze the crucial contributions of backbones with different configurations in FFD task and propose leveraging the ViT network with self-supervised learning on real-face datasets to pre-train a backbone, equipping it with superior facial representation capabilities.
Relaxed Rotational Equivariance via $G$-Biases in Vision
Group Equivariant Convolution (GConv) can capture rotational equivariance from original data.
SBDet: A Symmetry-Breaking Object Detector via Relaxed Rotation-Equivariance
Furthermore, we propose a Relaxed Rotation-Equivariant Network (R2Net) as the backbone and further develop the Symmetry-Breaking Object Detector (SBDet) for 2D object detection built upon it.
When Foresight Pruning Meets Zeroth-Order Optimization: Efficient Federated Learning for Low-Memory Devices
Comprehensive experimental results obtained from simulation- and real test-bed-based platforms show that our federated foresight-pruning method not only preserves the ability of the dense model with a memory reduction up to 9x but also boosts the performance of the vanilla BP-Free method with dramatically fewer FLOPs.
A PNP ion channel deep learning solver with local neural network and finite element input data
Since the input data of the neural network scheme only involves a small local patch of coarse grid solutions, which the finite element solver can quickly produce, the PNPic deep learning solver can be trained much faster than any corresponding conventional global neural network solvers.
Neural Network with Local Converging Input (NNLCI) for Supersonic Flow Problems with Unstructured Grids
In the present study, we develop a neural network with local converging input (NNLCI) for high-fidelity prediction using unstructured data.
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold
Although Deep Learning (DL) has achieved success in complex Artificial Intelligence (AI) tasks, it suffers from various notorious problems (e. g., feature redundancy, and vanishing or exploding gradients), since updating parameters in Euclidean space cannot fully exploit the geometric structure of the solution space.
Solving Maxwell's Equation in 2D with Neural Networks with Local Converging Inputs
In contrast with existing research work on applying neural networks to directly solve PDEs, our method takes advantage of the local domain of dependence of the Maxwell's equation in the input solution patches, and is therefore simpler, yet still robust.
SparGE: Sparse Coding-based Patient Similarity Learning via Low-rank Constraints and Graph Embedding
SparGE measures similarity by jointly sparse coding and graph embedding.
O-ViT: Orthogonal Vision Transformer
Inspired by the tremendous success of the self-attention mechanism in natural language processing, the Vision Transformer (ViT) creatively applies it to image patch sequences and achieves incredible performance.
Dense Multiscale Feature Fusion Pyramid Networks for Object Detection in UAV-Captured Images
Although much significant progress has been made in the research field of object detection with deep learning, there still exists a challenging task for the objects with small size, which is notably pronounced in UAV-captured images.
IDENT: Identifying Differential Equations with Numerical Time evolution
The new algorithm, called Identifying Differential Equations with Numerical Time evolution (IDENT), is explored for data with non-periodic boundary conditions, noisy data and PDEs with varying coefficients.
Numerical Analysis
