Search Results for author:
Found 8 papers, 4 papers with code
State Space Model for New-Generation Network Alternative to Transformers: A Survey
In this paper, we give the first comprehensive review of these works and also provide experimental comparisons and analysis to better demonstrate the features and advantages of SSM.
Which Model to Transfer? A Survey on Transferability Estimation
In this survey, we present the first review of existing advances in this area and categorize them into two separate realms: source-free model transferability estimation and source-dependent model transferability estimation.
Not all Minorities are Equal: Empty-Class-Aware Distillation for Heterogeneous Federated Learning
Data heterogeneity, characterized by disparities in local data distribution across clients, poses a significant challenge in federated learning.
Unleashing the power of Neural Collapse for Transferability Estimation
Transferability estimation aims to provide heuristics for quantifying how suitable a pre-trained model is for a specific downstream task, without fine-tuning them all.
MODIFY: Model-driven Face Stylization without Style Images
Briefly, MODIFY first trains a generative model in the target domain and then translates a source input to the target domain via the provided style model.
MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection
Existing cross-domain keypoint detection methods always require accessing the source data during adaptation, which may violate the data privacy law and pose serious security concerns.
ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptation
First of all, to avoid additional parameters and explore the information in the source model, ProxyMix defines the weights of the classifier as the class prototypes and then constructs a class-balanced proxy source domain by the nearest neighbors of the prototypes to bridge the unseen source domain and the target domain.
Unsupervised Contrastive Photo-to-Caricature Translation based on Auto-distortion
Considering the intuitive artifacts in the existing methods, we propose a contrastive style loss for style rendering to enforce the similarity between the style of rendered photo and the caricature, and simultaneously enhance its discrepancy to the photos.
