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 51–75 of 1751 papers

TitleStatusHype
DEBATE, TRAIN, EVOLVE: Self Evolution of Language Model Reasoning—0
seg_3D_by_PC2D: Multi-View Projection for Domain Generalization and Adaptation in 3D Semantic SegmentationCode0
Generalizable Multispectral Land Cover Classification via Frequency-Aware Mixture of Low-Rank Token Experts—0
Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training—0
Code2Logic: Game-Code-Driven Data Synthesis for Enhancing VLMs General ReasoningCode2
Towards A Generalist Code Embedding Model Based On Massive Data SynthesisCode0
Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement—0
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning—0
PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization—0
Learning Robust Spectral Dynamics for Temporal Domain Generalization—0
Enriching Patent Claim Generation with European Patent DatasetCode0
CLIP-aware Domain-Adaptive Super-Resolution—0
Continuous Domain Generalization—0
SoLoPO: Unlocking Long-Context Capabilities in LLMs via Short-to-Long Preference Optimization—0
On the Interplay of Human-AI Alignment,Fairness, and Performance Trade-offs in Medical ImagingCode0
Multi-Source Collaborative Style Augmentation and Domain-Invariant Learning for Federated Domain Generalization—0
CEC-Zero: Chinese Error Correction Solution Based on LLM—0
Denoising and Alignment: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing—0
Large Language Models Meet Stance Detection: A Survey of Tasks, Methods, Applications, Challenges and Future Directions—0
Accelerating Chain-of-Thought Reasoning: When Goal-Gradient Importance Meets Dynamic Skipping—0
Language-Driven Dual Style Mixing for Single-Domain Generalized Object DetectionCode0
NeuGen: Amplifying the 'Neural' in Neural Radiance Fields for Domain Generalization—0
Depth-Sensitive Soft Suppression with RGB-D Inter-Modal Stylization Flow for Domain Generalization Semantic Segmentation—0
NeuRN: Neuro-inspired Domain Generalization for Image Classification—0
Mice to Machines: Neural Representations from Visual Cortex for Domain Generalization—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