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 726750 of 1751 papers

TitleStatusHype
Exploring Flat Minima for Domain Generalization with Large Learning RatesCode0
ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target SimulationCode2
OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion DataCode1
INSURE: An Information Theory Inspired Disentanglement and Purification Model for Domain Generalization0
All Labels Together: Low-shot Intent Detection with an Efficient Label Semantic Encoding ParadigmCode0
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens0
SememeASR: Boosting Performance of End-to-End Speech Recognition against Domain and Long-Tailed Data Shift with Sememe Semantic Knowledge0
Generative Data Augmentation using LLMs improves Distributional Robustness in Question AnsweringCode3
LoGoPrompt: Synthetic Text Images Can Be Good Visual Prompts for Vision-Language Models0
Domain Generalization via Balancing Training Difficulty and Model Capability0
Domain Generalization without Excess Empirical Risk0
iBARLE: imBalance-Aware Room Layout Estimation0
Read-only Prompt Optimization for Vision-Language Few-shot LearningCode1
Pruning Self-Attention for Zero-Shot Multi-Speaker Text-to-Speech0
LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and RestorationCode0
Face Presentation Attack Detection by Excavating Causal Clues and Adapting Embedding StatisticsCode1
Multi-Scale and Multi-Layer Contrastive Learning for Domain GeneralizationCode0
Forensic Histopathological Recognition via a Context-Aware MIL Network Powered by Self-Supervised Contrastive LearningCode0
Exploring the Transfer Learning Capabilities of CLIP in Domain Generalization for Diabetic RetinopathyCode1
A Re-Parameterized Vision Transformer (ReVT) for Domain-Generalized Semantic SegmentationCode1
A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip DesignCode1
Understanding Hessian Alignment for Domain GeneralizationCode1
Unsupervised Prototype Adapter for Vision-Language Models0
GOPro: Generate and Optimize Prompts in CLIP using Self-Supervised LearningCode0
Domain Generalization via Rationale InvarianceCode1
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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