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 876–900 of 1751 papers

TitleStatusHype
CLST: Cold-Start Mitigation in Knowledge Tracing by Aligning a Generative Language Model as a Students' Knowledge Tracer—0
Minimizing Energy Costs in Deep Learning Model Training: The Gaussian Sampling Approach—0
On the Minimal Degree Bias in Generalization on the Unseen for non-Boolean FunctionsCode0
Causality-inspired Latent Feature Augmentation for Single Domain Generalization—0
Domain Agnostic Conditional Invariant Predictions for Domain Generalization—0
Domain Generalization Guided by Large-Scale Pre-Trained Priors—0
Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition—0
Attend and Enrich: Enhanced Visual Prompt for Zero-Shot Learning—0
Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation—0
ED-SAM: An Efficient Diffusion Sampling Approach to Domain Generalization in Vision-Language Foundation Models—0
Complex Style Image Transformations for Domain Generalization in Medical Images—0
Domain generalization for retinal vessel segmentation via Hessian-based vector field—0
Towards a Better Evaluation of Out-of-Domain Generalization—0
Lifelong Learning Using a Dynamically Growing Tree of Sub-networks for Domain Generalization in Video Object Segmentation—0
Domain-Inspired Sharpness-Aware Minimization Under Domain ShiftsCode0
WIDIn: Wording Image for Domain-Invariant Representation in Single-Source Domain Generalization—0
Grounding Stylistic Domain Generalization with Quantitative Domain Shift Measures and Synthetic Scene ImagesCode0
Exploring the Impact of Synthetic Data for Aerial-view Human Detection—0
Unbiased Faster R-CNN for Single-source Domain Generalized Object Detection—0
NuwaTS: a Foundation Model Mending Every Incomplete Time Series—0
Comparative Analysis of Different Efficient Fine Tuning Methods of Large Language Models (LLMs) in Low-Resource Setting—0
Benchmarking Cross-Domain Audio-Visual Deception Detection—0
Non-stationary Domain Generalization: Theory and Algorithm—0
PhysMLE: Generalizable and Priors-Inclusive Multi-task Remote Physiological Measurement—0
Joint semi-supervised and contrastive learning enables domain generalization and multi-domain segmentation—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