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 10261050 of 1751 papers

TitleStatusHype
HMOE: Hypernetwork-based Mixture of Experts for Domain Generalization0
Towards Generalization on Real Domain for Single Image Dehazing via Meta-Learning0
Generalization Beyond Feature Alignment: Concept Activation-Guided Contrastive Learning0
Cross-Platform and Cross-Domain Abusive Language Detection with Supervised Contrastive Learning0
DocuT5: Seq2seq SQL Generation with Table Documentation0
GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable PartsCode1
Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts0
FIXED: Frustratingly Easy Domain Generalization with Mixup0
Generalizable Re-Identification from Videos with Cycle Association0
Learning Causal Representations of Single Cells via Sparse Mechanism Shift ModelingCode1
Using Set Covering to Generate Databases for Holistic SteganalysisCode0
Contrastive Weighted Learning for Near-Infrared Gaze Estimation0
Learning to Learn Domain-invariant Parameters for Domain Generalization0
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling GeneralizationCode2
Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study0
Deep Multimodal Fusion for Generalizable Person Re-identificationCode0
Two-stage LLM Fine-tuning with Less Specialization and More Generalization0
SAGE: Saliency-Guided Mixup with Optimal Rearrangements0
Image-free Domain Generalization via CLIP for 3D Hand Pose Estimation0
Domain Generalization through the Lens of Angular InvarianceCode0
Efficient and Effective Augmentation Strategy for Adversarial TrainingCode1
Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Vision0
Trade-off between reconstruction loss and feature alignment for domain generalizationCode0
SimpleDG: Simple Domain Generalization Baseline without Bells and WhistlesCode0
OpenStance: Real-world Zero-shot Stance DetectionCode0
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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