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 101150 of 1751 papers

TitleStatusHype
Diversify Your Vision Datasets with Automatic Diffusion-Based AugmentationCode1
Domain-Adversarial Training of Neural NetworksCode1
Causality-inspired Single-source Domain Generalization for Medical Image SegmentationCode1
Adaptive High-Frequency Transformer for Diverse Wildlife Re-IdentificationCode1
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case GeneralizationCode1
Distribution Shift Inversion for Out-of-Distribution PredictionCode1
Which Invariance Should We Transfer? A Causal Minimax Learning ApproachCode1
CLIP the Gap: A Single Domain Generalization Approach for Object DetectionCode1
Aggregated Residual Transformations for Deep Neural NetworksCode1
Collaborating Foundation Models for Domain Generalized Semantic SegmentationCode1
Causal Balancing for Domain GeneralizationCode1
ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain GeneralizationCode1
Adaptive Risk Minimization: Learning to Adapt to Domain ShiftCode1
Cross Contrasting Feature Perturbation for Domain GeneralizationCode1
Causality Inspired Representation Learning for Domain GeneralizationCode1
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency DomainCode1
Consistency-guided Prompt Learning for Vision-Language ModelsCode1
DomainDrop: Suppressing Domain-Sensitive Channels for Domain GeneralizationCode1
A Closer Look at Few-shot ClassificationCode1
Anatomy of Domain Shift Impact on U-Net Layers in MRI SegmentationCode1
An Empirical Framework for Domain Generalization in Clinical SettingsCode1
A Fourier-based Framework for Domain GeneralizationCode1
UniDA3D: Unified Domain Adaptive 3D Semantic Segmentation PipelineCode1
Crafting Distribution Shifts for Validation and Training in Single Source Domain GeneralizationCode1
MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-LabelingCode1
An Information-theoretic Approach to Distribution ShiftsCode1
AFN: Adaptive Fusion Normalization via an Encoder-Decoder FrameworkCode1
CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image SegmentationCode1
Making Convolutional Networks Shift-Invariant AgainCode1
AdvST: Revisiting Data Augmentations for Single Domain GeneralizationCode1
CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from SimulationCode1
Cross-Domain Ensemble Distillation for Domain GeneralizationCode1
Distilling Out-of-Distribution Robustness from Vision-Language Foundation ModelsCode1
Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image SegmentationCode1
Domain generalization of 3D semantic segmentation in autonomous drivingCode1
Adversarial Training for Free!Code1
Bridging the Source-to-target Gap for Cross-domain Person Re-Identification with Intermediate DomainsCode1
Discovering environments with XRMCode1
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent AlignmentCode1
Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual LearningCode1
Adapting to Distribution Shift by Visual Domain Prompt GenerationCode1
DG-TTA: Out-of-domain Medical Image Segmentation through Augmentation and Descriptor-driven Domain Generalization and Test-Time AdaptationCode1
Calibrated Feature Decomposition for Generalizable Person Re-IdentificationCode1
Diffusion Features to Bridge Domain Gap for Semantic SegmentationCode1
Disentangled Feature Representation for Few-shot Image ClassificationCode1
Boosting Domain Generalized and Adaptive Detection with Diffusion Models: Fitness, Generalization, and TransferabilityCode1
Selecting Data Augmentation for Simulating InterventionsCode1
BioBridge: Bridging Biomedical Foundation Models via Knowledge GraphsCode1
A Broad Study of Pre-training for Domain Generalization and AdaptationCode1
Borrowing Knowledge From Pre-trained Language Model: A New Data-efficient Visual Learning ParadigmCode1
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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