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 151175 of 1751 papers

TitleStatusHype
Diffusion Features to Bridge Domain Gap for Semantic SegmentationCode1
Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual LearningCode1
Discovering environments with XRMCode1
DG-TTA: Out-of-domain Medical Image Segmentation through Augmentation and Descriptor-driven Domain Generalization and Test-Time AdaptationCode1
AugMix: A Simple Data Processing Method to Improve Robustness and UncertaintyCode1
DGMamba: Domain Generalization via Generalized State Space ModelCode1
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent AlignmentCode1
Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization TechniquesCode1
Attention Diversification for Domain GeneralizationCode1
DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image SegmentationCode1
Selecting Data Augmentation for Simulating InterventionsCode1
Attention Consistency on Visual Corruptions for Single-Source Domain GeneralizationCode1
Deep Stable Learning for Out-Of-Distribution GeneralizationCode1
DEJA VU: Continual Model Generalization For Unseen DomainsCode1
Benchmarking Distribution Shift in Tabular Data with TableShiftCode1
Augmenting Multi-Turn Text-to-SQL Datasets with Self-PlayCode1
A Bit More Bayesian: Domain-Invariant Learning with UncertaintyCode1
A Broad Study of Pre-training for Domain Generalization and AdaptationCode1
A Universal Representation Transformer Layer for Few-Shot Image ClassificationCode1
Devil is in Channels: Contrastive Single Domain Generalization for Medical Image SegmentationCode1
Disentangled Feature Representation for Few-shot Image ClassificationCode1
AutoGPart: Intermediate Supervision Search for Generalizable 3D Part SegmentationCode1
Domain-Adversarial Training of Neural NetworksCode1
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic AugmentationCode1
DATTA: Domain-Adversarial Test-Time Adaptation for Cross-Domain WiFi-Based Human Activity RecognitionCode1
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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