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 851875 of 1751 papers

TitleStatusHype
Consistency Regularization for Domain Generalization with Logit Attribution MatchingCode0
Masked Audio Text Encoders are Effective Multi-Modal Rescorers0
Multi-Prompt with Depth Partitioned Cross-Modal LearningCode0
Evaluating Embedding APIs for Information Retrieval0
Adaptive Domain Generalization for Digital Pathology Images0
PGrad: Learning Principal Gradients For Domain GeneralizationCode1
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensemblesCode0
SIMPLE: Specialized Model-Sample Matching for Domain GeneralizationCode0
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with RecommendationCode2
Moderately Distributional Exploration for Domain GeneralizationCode1
Domain Adaptive and Generalizable Network Architectures and Training Strategies for Semantic Image SegmentationCode2
EasyPortrait -- Face Parsing and Portrait Segmentation DatasetCode2
AdaNPC: Exploring Non-Parametric Classifier for Test-Time AdaptationCode1
Augmentation-based Domain Generalization for Semantic Segmentation0
Walking Your LiDOG: A Journey Through Multiple Domains for LiDAR Semantic SegmentationCode1
RPLKG: Robust Prompt Learning with Knowledge Graph0
Domain Generalization for Mammographic Image Analysis with Contrastive Learning0
Dual Stage Stylization Modulation for Domain Generalized Semantic Segmentation0
Decoding Neural Activity to Assess Individual Latent State in Ecologically Valid Contexts0
Chain of Thought Prompt Tuning in Vision Language Models0
DINOv2: Learning Robust Visual Features without SupervisionCode6
Frequency Decomposition to Tap the Potential of Single Domain for Generalization0
Out-of-distribution Few-shot Learning For Edge Devices without Model Fine-tuning0
Instance-Aware Domain Generalization for Face Anti-SpoofingCode1
Semantic-Aware Mixup for Domain GeneralizationCode0
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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