Search Results for author:
Found 13 papers, 3 papers with code
Isolated Diffusion: Optimizing Multi-Concept Text-to-Image Generation Training-Freely with Isolated Diffusion Guidance
This paper presents a general approach for text-to-image diffusion models to address the mutual interference between different subjects and their attachments in complex scenes, pursuing better text-image consistency.
Estimating On-road Transportation Carbon Emissions from Open Data of Road Network and Origin-destination Flow Data
To relieve this issue by utilizing the strong pattern recognition of artificial intelligence, we incorporate two sources of open data representative of the transportation demand and capacity factors, the origin-destination (OD) flow data and the road network data, to build a hierarchical heterogeneous graph learning method for on-road carbon emission estimation (HENCE).
Fovea Transformer: Efficient Long-Context Modeling with Structured Fine-to-Coarse Attention
The quadratic complexity of self-attention in Transformers has hindered the processing of long text.
DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited Data
Typical diffusion models and modern large-scale conditional generative models like text-to-image generative models are vulnerable to overfitting when fine-tuned on extremely limited data.
Few-shot 3D Shape Generation
Our approach only needs the silhouettes of few-shot target samples as training data to learn target geometry distributions and achieve generated shapes with diverse topology and textures.
Prediction with Incomplete Data under Agnostic Mask Distribution Shift
We focus on the case where the underlying joint distribution of complete features and label is invariant, but the missing pattern, i. e., mask distribution may shift agnostically between training and testing.
MotionVideoGAN: A Novel Video Generator Based on the Motion Space Learned from Image Pairs
We present MotionVideoGAN, a novel video generator synthesizing videos based on the motion space learned by pre-trained image pair generators.
Few-shot Image Generation with Diffusion Models
Then we fine-tune DDPMs pre-trained on large source domains to solve the overfitting problem when training data is limited.
Few-shot Image Generation via Masked Discrimination
It strengthens global image discrimination and guides adapted GANs to preserve more information learned from source domains for higher image quality.
Learning to Advise and Learning from Advice in Cooperative Multi-Agent Reinforcement Learning
In this paper, we explore the spatiotemporal structure of agents' decisions and consider the hierarchy of coordination from the perspective of multilevel emergence dynamics, based on which a novel approach, Learning to Advise and Learning from Advice (LALA), is proposed to improve MARL.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement learning
In multi-agent deep reinforcement learning, extracting sufficient and compact information of other agents is critical to attain efficient convergence and scalability of an algorithm.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Supervised Off-Policy Ranking
Inspired by the two observations, in this work, we study a new problem, supervised off-policy ranking (SOPR), which aims to rank a set of target policies based on supervised learning by leveraging off-policy data and policies with known performance.
