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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1676–1700 of 1751 papers

Title	Date	Tasks	Status	Hype
Tackling Partial Domain Adaptation with Self-Supervision	Jun 12, 2019	Domain AdaptationDomain Generalization	—Unverified	0
Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift	Jun 7, 2019	Domain AdaptationDomain Generalization	CodeCode Available	0
When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation	Jun 7, 2019	Data AugmentationDomain Adaptation	CodeCode Available	0
AutoAugment: Learning Augmentation Strategies From Data	Jun 1, 2019	Data AugmentationDomain Generalization	CodeCode Available	0
Domain Generalization by Solving Jigsaw Puzzles	Jun 1, 2019	Domain GeneralizationObject Recognition	CodeCode Available	0
Generalizable Person Re-Identification by Domain-Invariant Mapping Network	Jun 1, 2019	Domain GeneralizationGeneralizable Person Re-identification	—Unverified	0
Distant Supervised Centroid Shift: A Simple and Efficient Approach to Visual Domain Adaptation	Jun 1, 2019	Domain AdaptationDomain Generalization	—Unverified	0
Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection	Jun 1, 2019	Domain GeneralizationFace Anti-Spoofing	CodeCode Available	0
Learning Robust Global Representations by Penalizing Local Predictive Power	May 29, 2019	Domain AdaptationDomain Generalization	CodeCode Available	1
Domain Generalization via Universal Non-volume Preserving Models	May 28, 2019	Domain AdaptationDomain Generalization	—Unverified	0
A Cross-Domain Transferable Neural Coherence Model	May 28, 2019	ArticlesDomain Generalization	CodeCode Available	0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks	May 28, 2019	Action RecognitionDomain Generalization	CodeCode Available	3
DIVA: Domain Invariant Variational Autoencoders	May 24, 2019	Domain GeneralizationRotated MNIST	CodeCode Available	1
A Generalization Error Bound for Multi-class Domain Generalization	May 24, 2019	ClassificationDomain Generalization	—Unverified	0
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features	May 13, 2019	Domain GeneralizationImage Captioning	CodeCode Available	1
Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization	Apr 29, 2019	Domain Generalization	—Unverified	0
Adversarial Training for Free!	Apr 29, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	1
Making Convolutional Networks Shift-Invariant Again	Apr 25, 2019	Classification ConsistencyConditional Image Generation	CodeCode Available	1
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting	Apr 23, 2019	Domain GeneralizationGeneralizable Person Re-identification	—Unverified	0
Predicting human decisions with behavioral theories and machine learning	Apr 15, 2019	BIG-bench Machine LearningDecision Making	—Unverified	0
A Closer Look at Few-shot Classification	Apr 8, 2019	Domain GeneralizationFew-Shot Learning	CodeCode Available	1
CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth	Apr 3, 2019	3D Depth EstimationDepth Estimation	CodeCode Available	0
Improving Cross-Corpus Speech Emotion Recognition with Adversarial Discriminative Domain Generalization (ADDoG)	Mar 28, 2019	Cross-corpusDomain Generalization	—Unverified	0
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations	Mar 28, 2019	Adversarial DefenseBenchmarking	CodeCode Available	2
DIVA: Domain Invariant Variational Autoencoder	Mar 27, 2019	Domain GeneralizationRotated MNIST	—Unverified	0

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All datasets PACS VizWiz-Classification ImageNet-C Office-Home ImageNet-A ImageNet-R DomainNet VLCS TerraIncognita GTA-to-Avg(Cityscapes,BDD,Mapillary)ImageNet-Sketch GTA5 to Cityscapes

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	SIMPLE+	Average Accuracy	99	—	Unverified
2	PromptStyler (CLIP, ViT-L/14)	Average Accuracy	98.6	—	Unverified
3	GMDG (RegNetY-16GF, SWAD)	Average Accuracy	97.9	—	Unverified
4	D-Triplet(RegNetY-16GF)	Average Accuracy	97.6	—	Unverified
5	MoA (OpenCLIP, ViT-B/16)	Average Accuracy	97.4	—	Unverified
6	GMDG (e RegNetY-16GF)	Average Accuracy	97.3	—	Unverified
7	PromptStyler (CLIP, ViT-B/16)	Average Accuracy	97.2	—	Unverified
8	SPG (CLIP, ViT-B/16)	Average Accuracy	97	—	Unverified
9	CAR-FT (CLIP, ViT-B/16)	Average Accuracy	96.8	—	Unverified
10	MIRO (RegNetY-16GF, SWAD)	Average Accuracy	96.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ViT-8/B-224	Accuracy - Clean Images	450	—	Unverified
2	VOLO-D5	Accuracy - All Images	57.2	—	Unverified
3	ConvNeXt-B	Accuracy - All Images	53.5	—	Unverified
4	ResNeXt-101 32x16d	Accuracy - All Images	51.7	—	Unverified
5	EfficientNet-B8 (advprop+autoaug)	Accuracy - All Images	50.5	—	Unverified
6	EfficientNet-B7 (advprop+autoaug)	Accuracy - All Images	49.7	—	Unverified
7	EfficientNet-B6 (advprop+autoaug)	Accuracy - All Images	49.6	—	Unverified
8	EfficientNet-B5 (advprop+autoaug)	Accuracy - All Images	49.1	—	Unverified
9	ViT-16/L-224	Accuracy - All Images	49	—	Unverified
10	ResNet-50 (gn)	Accuracy - All Images	48.9	—	Unverified