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 201225 of 1751 papers

TitleStatusHype
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity RecognitionCode1
Aggregated Residual Transformations for Deep Neural NetworksCode1
AdaNPC: Exploring Non-Parametric Classifier for Test-Time AdaptationCode1
DG-TTA: Out-of-domain Medical Image Segmentation through Augmentation and Descriptor-driven Domain Generalization and Test-Time AdaptationCode1
Beyond Model Adaptation at Test Time: A SurveyCode1
Beyond Normal: On the Evaluation of Mutual Information EstimatorsCode1
Adaptive Methods for Aggregated Domain GeneralizationCode1
Domain-Specific Bias Filtering for Single Labeled Domain GeneralizationCode1
Domain-Unified Prompt Representations for Source-Free Domain GeneralizationCode1
BioBridge: Bridging Biomedical Foundation Models via Knowledge GraphsCode1
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent AlignmentCode1
Discovering environments with XRMCode1
Federated Domain Generalization With Generalization AdjustmentCode1
Boosting Domain Generalized and Adaptive Detection with Diffusion Models: Fitness, Generalization, and TransferabilityCode1
Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical ImagingCode1
Disentangled Feature Representation for Few-shot Image ClassificationCode1
Borrowing Knowledge From Pre-trained Language Model: A New Data-efficient Visual Learning ParadigmCode1
Disentangling Masked Autoencoders for Unsupervised Domain GeneralizationCode1
Domain-Generalized Face Anti-Spoofing with Unknown AttacksCode1
Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain DatasetsCode1
Distilling Out-of-Distribution Robustness from Vision-Language Foundation ModelsCode1
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case GeneralizationCode1
Domain Invariant Representation Learning with Domain Density TransformationsCode1
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
Contrastive Syn-to-Real GeneralizationCode1
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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