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 876900 of 1751 papers

TitleStatusHype
APPLeNet: Visual Attention Parameterized Prompt Learning for Few-Shot Remote Sensing Image Generalization using CLIPCode1
Improved Test-Time Adaptation for Domain GeneralizationCode1
Supervised Contrastive Learning with Heterogeneous Similarity for Distribution Shifts0
Meta-causal Learning for Single Domain Generalization0
Domain Generalization In Robust Invariant RepresentationCode0
Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic SegmentationCode1
Multi-view Adversarial Discriminator: Mine the Non-causal Factors for Object Detection in Unseen DomainsCode1
Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation0
Randomized Adversarial Style Perturbations for Domain Generalization0
EPVT: Environment-aware Prompt Vision Transformer for Domain Generalization in Skin Lesion RecognitionCode1
ERM++: An Improved Baseline for Domain GeneralizationCode1
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue DistributionCode1
Domain Generalization for Crop Segmentation with Standardized Ensemble Knowledge DistillationCode0
A principled approach to model validation in domain generalizationCode0
Progressive Random Convolutions for Single Domain Generalization0
CNNs with Multi-Level Attention for Domain Generalization0
Simple Domain Generalization Methods are Strong Baselines for Open Domain GeneralizationCode0
Your Diffusion Model is Secretly a Zero-Shot ClassifierCode2
Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning0
TFS-ViT: Token-Level Feature Stylization for Domain GeneralizationCode1
SDTracker: Synthetic Data Based Multi-Object Tracking0
VisDA 2022 Challenge: Domain Adaptation for Industrial Waste SortingCode1
Rethinking Domain Generalization for Face Anti-spoofing: Separability and AlignmentCode1
Improving Generalization with Domain Convex GameCode0
MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain GeneralizationCode1
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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