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 226250 of 1751 papers

TitleStatusHype
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
DG-TTA: Out-of-domain Medical Image Segmentation through Augmentation and Descriptor-driven Domain Generalization and Test-Time AdaptationCode1
FSDR: Frequency Space Domain Randomization for Domain GeneralizationCode1
Domain Prompt Learning for Efficiently Adapting CLIP to Unseen DomainsCode1
Generalizable Decision Boundaries: Dualistic Meta-Learning for Open Set Domain GeneralizationCode1
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency DomainCode1
Generalizable Model-agnostic Semantic Segmentation via Target-specific NormalizationCode1
Generalized Diffusion Detector: Mining Robust Features from Diffusion Models for Domain-Generalized DetectionCode1
CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from SimulationCode1
DGMamba: Domain Generalization via Generalized State Space ModelCode1
Selecting Data Augmentation for Simulating InterventionsCode1
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
Devil is in Channels: Contrastive Single Domain Generalization for Medical Image SegmentationCode1
Anatomy of Domain Shift Impact on U-Net Layers in MRI SegmentationCode1
Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual LearningCode1
Causality Inspired Representation Learning for Domain GeneralizationCode1
Causality-inspired Single-source Domain Generalization for Medical Image SegmentationCode1
Which Invariance Should We Transfer? A Causal Minimax Learning ApproachCode1
Gradient-Guided Annealing for Domain GeneralizationCode1
CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image SegmentationCode1
UniDA3D: Unified Domain Adaptive 3D Semantic Segmentation PipelineCode1
HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic SegmentationCode1
Disentangled Feature Representation for Few-shot Image ClassificationCode1
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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