Search Results for author:
Found 18 papers, 4 papers with code
CACA Agent: Capability Collaboration based AI Agent
As AI Agents based on Large Language Models (LLMs) have shown potential in practical applications across various fields, how to quickly deploy an AI agent and how to conveniently expand the application scenario of AI agents has become a challenge.
Ensemble Quadratic Assignment Network for Graph Matching
In this paper, we propose a graph neural network (GNN) based approach to combine the advantages of data-driven and traditional methods.
Faster Projected GAN: Towards Faster Few-Shot Image Generation
In order to solve the problems of long training time, large consumption of computing resources and huge parameter amount of GAN network in image generation, this paper proposes an improved GAN network model, which is named Faster Projected GAN, based on Projected GAN.
SVGDreamer: Text Guided SVG Generation with Diffusion Model
However, existing text-to-SVG generation methods lack editability and struggle with visual quality and result diversity.
Inversion-by-Inversion: Exemplar-based Sketch-to-Photo Synthesis via Stochastic Differential Equations without Training
In the full-control inversion process, we propose an appearance-energy function to control the color and texture of the final generated photo. Importantly, our Inversion-by-Inversion pipeline is training-free and can accept different types of exemplars for color and texture control.
Class Incremental Learning with Self-Supervised Pre-Training and Prototype Learning
This is hard for DNN because it tends to focus on fitting to new classes while ignoring old classes, a phenomenon known as catastrophic forgetting.
DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models
Even though trained mainly on images, we discover that pretrained diffusion models show impressive power in guiding sketch synthesis.
Proxy Graph Matching with Proximal Matching Networks
It consists of three parts: a graph neural network to generate a high-level local feature, an attention-based module to normalize the rotational transform, and a global feature matching module based on proximal optimization.
Prototype Augmentation and Self-Supervision for Incremental Learning
Despite the impressive performance in many individual tasks, deep neural networks suffer from catastrophic forgetting when learning new tasks incrementally.
Misclassification Detection via Class Augmentation
In spite of the simplicity, extensive experiments demonstrate that the misclassification detection performance of DNNs can be significantly improved by seeing more generated pseudo-classes during training.
Restrained Generative Adversarial Network against Overfitting in Numeric Data Augmentation
In recent studies, Generative Adversarial Network (GAN) is one of the popular schemes to augment the image dataset.
FISHING Net: Future Inference of Semantic Heatmaps In Grids
Managing the different reference frames and characteristics of the sensors, and merging their observations into a single representation complicates perception.
Lossless Attention in Convolutional Networks for Facial Expression Recognition in the Wild
And we validate our method on the Aff-Wild2 datasets released by the Challenge.
Facial Expression Recognition
Facial Expression Recognition (FER)
Study of Constrained Network Structures for WGANs on Numeric Data Generation
Especially, Isomorphic WGAN is the best in 15/20 experiments.
A Solvable High-Dimensional Model of GAN
We present a theoretical analysis of the training process for a single-layer GAN fed by high-dimensional input data.
Subspace Estimation from Incomplete Observations: A High-Dimensional Analysis
In addition to providing asymptotically exact predictions of the dynamic performance of the algorithms, our high-dimensional analysis yields several insights, including an asymptotic equivalence between Oja's method and GROUSE, and a precise scaling relationship linking the amount of missing data to the signal-to-noise ratio.
Scaling Limit: Exact and Tractable Analysis of Online Learning Algorithms with Applications to Regularized Regression and PCA
In addition to characterizing the dynamic performance of online learning algorithms, our asymptotic analysis also provides useful insights.
The Scaling Limit of High-Dimensional Online Independent Component Analysis
As the ambient dimension tends to infinity, and with proper time scaling, we show that the time-varying joint empirical measure of the target feature vector and the estimates provided by the algorithm will converge weakly to a deterministic measured-valued process that can be characterized as the unique solution of a nonlinear PDE.
