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 176–200 of 1751 papers

TitleStatusHype
When and How Does CLIP Enable Domain and Compositional Generalization?—0
A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites—0
Supervised Contrastive Block Disentanglement—0
Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation—0
Color-Quality Invariance for Robust Medical Image SegmentationCode0
Single-Domain Generalized Object Detection by Balancing Domain Diversity and Invariance—0
Rotation-Adaptive Point Cloud Domain Generalization via Intricate Orientation Learning—0
Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose EstimationCode2
Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment—0
Learning to Learn Weight Generation via Local Consistency Diffusion—0
Intrinsic Tensor Field Propagation in Large Language Models: A Novel Approach to Contextual Information Flow—0
Test-time Loss Landscape Adaptation for Zero-Shot Generalization in Vision-Language Models—0
Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation ModelsCode3
Technical report on label-informed logit redistribution for better domain generalization in low-shot classification with foundation models—0
RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token ReprogrammingsCode1
FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment—0
3DLabelProp: Geometric-Driven Domain Generalization for LiDAR Semantic Segmentation in Autonomous DrivingCode1
Federated Domain Generalization with Data-free On-server Matching Gradient—0
Rethinking Table Instruction TuningCode0
Mitigating Forgetting in LLM Fine-Tuning via Low-Perplexity Token Learning—0
Autonomous Structural Memory Manipulation for Large Language Models Using Hierarchical Embedding Augmentation—0
FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis—0
Avoiding Shortcuts: Enhancing Channel-Robust Specific Emitter Identification via Single-Source Domain GeneralizationCode2
CLAP-S: Support Set Based Adaptation for Downstream Fiber-optic Acoustic RecognitionCode0
Learning to Adapt Frozen CLIP for Few-Shot Test-Time Domain AdaptationCode0
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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