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 11761200 of 1751 papers

TitleStatusHype
Improving Pre-trained Language Model Fine-tuning with Noise Stability Regularization0
APT-36K: A Large-scale Benchmark for Animal Pose Estimation and TrackingCode1
Toward Real-world Single Image Deraining: A New Benchmark and BeyondCode1
Referring Image MattingCode2
Causal Balancing for Domain GeneralizationCode1
Sparse Mixture-of-Experts are Domain Generalizable LearnersCode1
OneRing: A Simple Method for Source-free Open-partial Domain AdaptationCode1
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods0
Evolving Domain Generalization0
Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classification0
Dynamic Domain GeneralizationCode1
MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization TaskCode1
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias ReductionCode1
Supporting Vision-Language Model Inference with Causality-pruning Knowledge Prompt0
Temporal Domain Generalization with Drift-Aware Dynamic Neural NetworksCode1
Test-time Batch Normalization0
Generalizing to Evolving Domains with Latent Structure-Aware Sequential AutoencoderCode0
Not to Overfit or Underfit the Source Domains? An Empirical Study of Domain Generalization in Question Answering0
Test-time Fourier Style Calibration for Domain GeneralizationCode1
Contrastive Domain Disentanglement for Generalizable Medical Image Segmentation0
Localized Adversarial Domain GeneralizationCode1
Invariant Content Synergistic Learning for Domain Generalization of Medical Image Segmentation0
InvNorm: Domain Generalization for Object Detection in Gastrointestinal Endoscopy0
EllSeg-Gen, towards Domain Generalization for head-mounted eyetrackingCode0
Sequencer: Deep LSTM for Image ClassificationCode5
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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