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

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Showing 1026–1050 of 1751 papers

TitleStatusHype
Attend and Enrich: Enhanced Visual Prompt for Zero-Shot Learning—0
INSURE: An Information Theory Inspired Disentanglement and Purification Model for Domain Generalization—0
Integrated Structural Prompt Learning for Vision-Language Models—0
Integrating Audio Narrations to Strengthen Domain Generalization in Multimodal First-Person Action Recognition—0
Internal Structure Attention Network for Fingerprint Presentation Attack Detection from Optical Coherence Tomography—0
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting—0
Interpreting What Typical Fault Signals Look Like via Prototype-matching—0
In the Era of Prompt Learning with Vision-Language Models—0
Intrinsic Tensor Field Propagation in Large Language Models: A Novel Approach to Contextual Information Flow—0
Invariant Batch Normalization for Multi-source Domain Generalization—0
Invariant Causal Mechanisms through Distribution Matching—0
Invariant Content Synergistic Learning for Domain Generalization of Medical Image Segmentation—0
Invariant Correlation of Representation with Label—0
Invariant Information Bottleneck for Domain Generalization—0
Invariant Language Modeling—0
InvariantOODG: Learning Invariant Features of Point Clouds for Out-of-Distribution Generalization—0
End-to-End Lyrics Recognition with Self-supervised Learning—0
InvNorm: Domain Generalization for Object Detection in Gastrointestinal Endoscopy—0
Is Generative Modeling-based Stylization Necessary for Domain Adaptation in Regression Tasks?—0
Is Large-Scale Pretraining the Secret to Good Domain Generalization?—0
Is Multiple Object Tracking a Matter of Specialization?—0
FedWon: Triumphing Multi-domain Federated Learning Without Normalization—0
Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments—0
Joint Semi-supervised 3D Super-Resolution and Segmentation with Mixed Adversarial Gaussian Domain Adaptation—0
Joint semi-supervised and contrastive learning enables domain generalization and multi-domain segmentation—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