SOTAVerified

Domain Generalization

The idea of Domain Generalization is to learn from one or multiple training domains, to extract a domain-agnostic model which can be applied to an unseen domain

Source: Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning

Papers

Showing 751775 of 1751 papers

TitleStatusHype
Image-free Domain Generalization via CLIP for 3D Hand Pose Estimation0
Frequency Decomposition to Tap the Potential of Single Domain for Generalization0
Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation0
Federated Learning with Domain Generalization0
DEBATE, TRAIN, EVOLVE: Self Evolution of Language Model Reasoning0
Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training0
Federated Domain Generalization with Data-free On-server Matching Gradient0
From One to the Power of Many: Invariance to Multi-LiDAR Perception from Single-Sensor Datasets0
Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations0
AXCEL: Automated eXplainable Consistency Evaluation using LLMs0
Federated Domain Generalization: A Survey0
Adapting Large Language Models for Multi-Domain Retrieval-Augmented-Generation0
Improve Unsupervised Domain Adaptation with Mixup Training0
Improving the Generalization of Meta-learning on Unseen Domains via Adversarial Shift0
Fully Test-Time rPPG Estimation via Synthetic Signal-Guided Feature Learning0
Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems0
Federated and Generalized Person Re-identification through Domain and Feature Hallucinating0
DaSeGAN: Domain Adaptation for Segmentation Tasks via Generative Adversarial Networks0
FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis0
Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization0
DANSK and DaCy 2.6.0: Domain Generalization of Danish Named Entity Recognition0
GBlobs: Explicit Local Structure via Gaussian Blobs for Improved Cross-Domain LiDAR-based 3D Object Detection0
Autonomous Structural Memory Manipulation for Large Language Models Using Hierarchical Embedding Augmentation0
GDDS: A Single Domain Generalized Defect Detection Frame of Open World Scenario using Gather and Distribute Domain-shift Suppression Network0
How Important are Data Augmentations to Close the Domain Gap for Object Detection in Orbit?0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SIMPLE+Average Accuracy99Unverified
2PromptStyler (CLIP, ViT-L/14)Average Accuracy98.6Unverified
3GMDG (RegNetY-16GF, SWAD)Average Accuracy97.9Unverified
4D-Triplet(RegNetY-16GF)Average Accuracy97.6Unverified
5MoA (OpenCLIP, ViT-B/16)Average Accuracy97.4Unverified
6GMDG (e RegNetY-16GF)Average Accuracy97.3Unverified
7PromptStyler (CLIP, ViT-B/16)Average Accuracy97.2Unverified
8SPG (CLIP, ViT-B/16)Average Accuracy97Unverified
9CAR-FT (CLIP, ViT-B/16)Average Accuracy96.8Unverified
10MIRO (RegNetY-16GF, SWAD)Average Accuracy96.8Unverified
#ModelMetricClaimedVerifiedStatus
1ViT-8/B-224Accuracy - Clean Images450Unverified
2VOLO-D5Accuracy - All Images57.2Unverified
3ConvNeXt-BAccuracy - All Images53.5Unverified
4ResNeXt-101 32x16dAccuracy - All Images51.7Unverified
5EfficientNet-B8 (advprop+autoaug)Accuracy - All Images50.5Unverified
6EfficientNet-B7 (advprop+autoaug)Accuracy - All Images49.7Unverified
7EfficientNet-B6 (advprop+autoaug)Accuracy - All Images49.6Unverified
8EfficientNet-B5 (advprop+autoaug)Accuracy - All Images49.1Unverified
9ViT-16/L-224Accuracy - All Images49Unverified
10ResNet-50 (gn)Accuracy - All Images48.9Unverified