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 426450 of 1751 papers

TitleStatusHype
Generalize then Adapt: Source-Free Domain Adaptive Semantic SegmentationCode1
Adaptive Multi-Modal Cross-Entropy Loss for Stereo MatchingCode1
Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image SegmentationCode1
Leveraging Vision-Language Models for Improving Domain Generalization in Image ClassificationCode1
Domain Decorrelation with Potential Energy RankingCode1
Distilling Out-of-Distribution Robustness from Vision-Language Foundation ModelsCode1
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case GeneralizationCode1
Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain GeneralizationCode1
Distribution Shift Inversion for Out-of-Distribution PredictionCode1
DIVA: Domain Invariant Variational AutoencodersCode1
Borrowing Knowledge From Pre-trained Language Model: A New Data-efficient Visual Learning ParadigmCode1
G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object DetectionCode1
Invariant-Feature Subspace Recovery: A New Class of Provable Domain Generalization AlgorithmsCode1
Adaptive Network Combination for Single-Image Reflection Removal: A Domain Generalization PerspectiveCode1
Gradient-Guided Annealing for Domain GeneralizationCode1
Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain DatasetsCode1
Diversify Your Vision Datasets with Automatic Diffusion-Based AugmentationCode1
Group-wise Inhibition based Feature Regularization for Robust ClassificationCode1
RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective WhiteningCode1
Grounding Visual Representations with Texts for Domain GeneralizationCode1
Domain-Adversarial Training of Neural NetworksCode1
Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution GeneralizationCode1
HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic SegmentationCode1
Bridging the Source-to-target Gap for Cross-domain Person Re-Identification with Intermediate DomainsCode1
Domain Generalization using Causal MatchingCode1
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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