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 101–125 of 1751 papers

TitleStatusHype
Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization TechniquesCode1
Adaptive High-Frequency Transformer for Diverse Wildlife Re-IdentificationCode1
Aggregated Residual Transformations for Deep Neural NetworksCode1
Beyond Model Adaptation at Test Time: A SurveyCode1
BioBridge: Bridging Biomedical Foundation Models via Knowledge GraphsCode1
Bridging the Source-to-target Gap for Cross-domain Person Re-Identification with Intermediate DomainsCode1
Benchmarking Distribution Shift in Tabular Data with TableShiftCode1
Boosting Domain Generalized and Adaptive Detection with Diffusion Models: Fitness, Generalization, and TransferabilityCode1
Adaptive Network Combination for Single-Image Reflection Removal: A Domain Generalization PerspectiveCode1
Borrowing Knowledge From Pre-trained Language Model: A New Data-efficient Visual Learning ParadigmCode1
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity RecognitionCode1
ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain GeneralizationCode1
Adaptive Risk Minimization: Learning to Adapt to Domain ShiftCode1
Calibrated Feature Decomposition for Generalizable Person Re-IdentificationCode1
Domain Prompt Learning for Efficiently Adapting CLIP to Unseen DomainsCode1
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency DomainCode1
A Fourier-based Framework for Domain GeneralizationCode1
DG-TTA: Out-of-domain Medical Image Segmentation through Augmentation and Descriptor-driven Domain Generalization and Test-Time AdaptationCode1
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
Causality-inspired Single-source Domain Generalization for Medical Image SegmentationCode1
AFN: Adaptive Fusion Normalization via an Encoder-Decoder FrameworkCode1
CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image SegmentationCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SIMPLE+Average Accuracy99—Unverified
2PromptStyler (CLIP, ViT-L/14)Average Accuracy98.6—Unverified
3GMDG (RegNetY-16GF, SWAD)Average Accuracy97.9—Unverified
4D-Triplet(RegNetY-16GF)Average Accuracy97.6—Unverified
5MoA (OpenCLIP, ViT-B/16)Average Accuracy97.4—Unverified
6GMDG (e RegNetY-16GF)Average Accuracy97.3—Unverified
7PromptStyler (CLIP, ViT-B/16)Average Accuracy97.2—Unverified
8SPG (CLIP, ViT-B/16)Average Accuracy97—Unverified
9MIRO (RegNetY-16GF, SWAD)Average Accuracy96.8—Unverified
10CAR-FT (CLIP, ViT-B/16)Average Accuracy96.8—Unverified
#ModelMetricClaimedVerifiedStatus
1ViT-8/B-224Accuracy - Clean Images450—Unverified
2VOLO-D5Accuracy - All Images57.2—Unverified
3ConvNeXt-BAccuracy - All Images53.5—Unverified
4ResNeXt-101 32x16dAccuracy - All Images51.7—Unverified
5EfficientNet-B8 (advprop+autoaug)Accuracy - All Images50.5—Unverified
6EfficientNet-B7 (advprop+autoaug)Accuracy - All Images49.7—Unverified
7EfficientNet-B6 (advprop+autoaug)Accuracy - All Images49.6—Unverified
8EfficientNet-B5 (advprop+autoaug)Accuracy - All Images49.1—Unverified
9ViT-16/L-224Accuracy - All Images49—Unverified
10ResNet-50 (gn)Accuracy - All Images48.9—Unverified