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 1001–1025 of 1751 papers

TitleStatusHype
BMIP: Bi-directional Modality Interaction Prompt Learning for VLM—0
BoosterNet: Improving Domain Generalization of Deep Neural Nets Using Culpability-Ranked Features—0
Boosting Federated Domain Generalization: Understanding the Role of Advanced Pre-Trained Architectures—0
Boundless Across Domains: A New Paradigm of Adaptive Feature and Cross-Attention for Domain Generalization in Medical Image Segmentation—0
Brain-Inspired Online Adaptation for Remote Sensing with Spiking Neural Network—0
Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations—0
Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion—0
C^3DG: Conditional Domain Generalization for Hyperspectral Imagery Classification with Convergence and Constrained-risk Theories—0
CADG: A Model Based on Cross Attention for Domain Generalization—0
Camera-Invariant Meta-Learning Network for Single-Camera-Training Person Re-identification—0
Can a Frozen Pretrained Language Model be used for Zero-shot Neural Retrieval on Entity-centric Questions?—0
CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization—0
CauDR: A Causality-inspired Domain Generalization Framework for Fundus-based Diabetic Retinopathy Grading—0
Causal-based Time Series Domain Generalization for Vehicle Intention Prediction—0
Causal Feature Selection for Responsible Machine Learning—0
Causality-based Dual-Contrastive Learning Framework for Domain Generalization—0
Causality-inspired Latent Feature Augmentation for Single Domain Generalization—0
Causal Representation-Based Domain Generalization on Gaze Estimation—0
CEC-Zero: Chinese Error Correction Solution Based on LLM—0
Center-aware Adversarial Augmentation for Single Domain Generalization—0
CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofing—0
Chain-of-Thought Matters: Improving Long-Context Language Models with Reasoning Path Supervision—0
Chain-of-Thought Prompt Distillation for Multimodal Named Entity Recognition and Multimodal Relation Extraction—0
Chain of Thought Prompt Tuning in Vision Language Models—0
Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering—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