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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 826–850 of 1751 papers

TitleStatusHype
Dataset of Random Relaxations for Crystal Structure Search of Li-Si System—0
Adapting In-Domain Few-Shot Segmentation to New Domains without Retraining—0
GLAD: Generalizable Tuning for Vision-Language Models—0
Internal Structure Attention Network for Fingerprint Presentation Attack Detection from Optical Coherence Tomography—0
Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation—0
FAMLP: A Frequency-Aware MLP-Like Architecture For Domain Generalization—0
Cross-Task Pretraining for Cross-Organ Cross-Scanner Adenocarcinoma Segmentation—0
Integrated Structural Prompt Learning for Vision-Language Models—0
Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization—0
INSURE: An Information Theory Inspired Disentanglement and Purification Model for Domain Generalization—0
Integrating Audio Narrations to Strengthen Domain Generalization in Multimodal First-Person Action Recognition—0
Gradient Estimation for Unseen Domain Risk Minimization with Pre-Trained Models—0
Cross-Platform and Cross-Domain Abusive Language Detection with Supervised Contrastive Learning—0
Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging—0
Instance Paradigm Contrastive Learning for Domain Generalization—0
FADE: Towards Fairness-aware Augmentation for Domain Generalization via Classifier-Guided Score-based Diffusion Models—0
Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models—0
Cross-Modal Concept Learning and Inference for Vision-Language Models—0
CrossMatch: Cross-Classifier Consistency Regularization for Open-Set Single Domain Generalization—0
Crossing the Gap: Domain Generalization for Image Captioning—0
Learning to Augment via Implicit Differentiation for Domain Generalization—0
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods—0
A Unified Framework for Robustness on Diverse Sampling Errors—0
Attend and Enrich: Enhanced Visual Prompt for Zero-Shot Learning—0
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting—0
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All datasetsPACSVizWiz-ClassificationImageNet-COffice-HomeImageNet-AImageNet-RDomainNetVLCSTerraIncognitaGTA-to-Avg(Cityscapes,BDD,Mapillary)ImageNet-SketchGTA5 to Cityscapes

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
9CAR-FT (CLIP, ViT-B/16)Average Accuracy96.8—Unverified
10MIRO (RegNetY-16GF, SWAD)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