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 14261450 of 1751 papers

TitleStatusHype
UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception DetectionCode0
Collaborative Semantic Aggregation and Calibration for Federated Domain GeneralizationCode0
Domain Generalization via Nuclear Norm RegularizationCode0
Domain Generalization via Model-Agnostic Learning of Semantic FeaturesCode0
Meta-Reasoning: Semantics-Symbol Deconstruction for Large Language ModelsCode0
CLAP-S: Support Set Based Adaptation for Downstream Fiber-optic Acoustic RecognitionCode0
Choosing Wisely and Learning Deeply: Selective Cross-Modality Distillation via CLIP for Domain GeneralizationCode0
Certifying Out-of-Domain Generalization for Blackbox FunctionsCode0
SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain GeneralizationCode0
Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity RecognitionCode0
CAMME: Adaptive Deepfake Image Detection with Multi-Modal Cross-AttentionCode0
Learn to Preserve and Diversify: Parameter-Efficient Group with Orthogonal Regularization for Domain GeneralizationCode0
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI ModelsCode0
The Data Challenge in Misinformation Detection: Source Reputation vs. Content VeracityCode0
Learning to Learn Single Domain GeneralizationCode0
CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View DepthCode0
Mitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented CalibrationCode0
1st Place Solution for ECCV 2022 OOD-CV Challenge Object Detection TrackCode0
Learning to Generalize towards Unseen Domains via a Content-Aware Style Invariant Model for Disease Detection from Chest X-raysCode0
Domain generalization via invariant feature representationCode0
Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain GeneralizationCode0
Calibration-Free Driver Drowsiness Classification based on Manifold-Level AugmentationCode0
Domain Generalization Using a Mixture of Multiple Latent DomainsCode0
The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain GeneralizationCode0
Unified Deep Supervised Domain Adaptation and 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