https://github.com/modestyachts/imagenet-testbed
공식 코드Measuring Robustness to Natural Distribution Shifts in Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Measuring Robustness to Natural Distribution Shifts in Image Classification
Benchmarking Domain Generalization Algorithms in Computational Pathology
ControlMLLM: Training-Free Visual Prompt Learning for Multimodal Large Language Models
Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment
Grounding Visual Representations with Texts for Domain Generalization
A Study on Domain Generalization for Failure Detection through Human Reactions in HRI
Domain-Unified Prompt Representations for Source-Free Domain Generalization
Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss
Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts
Distilling LLM Agent into Small Models with Retrieval and Code Tools
Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation
LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration
Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation
Crafting Distribution Shifts for Validation and Training in Single Source Domain Generalization
PaPaGei: Open Foundation Models for Optical Physiological Signals
Clustering-based Image-Text Graph Matching for Domain Generalization
Learning Beyond Experience: Generalizing to Unseen State Space with Reservoir Computing
Frequency-Based Federated Domain Generalization for Polyp Segmentation
Generalized Trajectory Scoring for End-to-end Multimodal Planning
Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing
Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image Classification
The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization
APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking
APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.