HyperAI
HyperAI
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تسجيل الدخول
الرئيسية
SOTA
كشف الأخطاء
Anomaly Detection On One Class Imagenet 30
Anomaly Detection On One Class Imagenet 30
المقاييس
AUROC
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار المرجعي
Columns
اسم النموذج
AUROC
عنوان الورقة
Code
BCE-Clip (OE)
99.90
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
CLIP (Zero Shot)
99.88
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
Binary Cross Entropy (OE)
97.7
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
CSI
91.6
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
FCDD
91
Explainable Deep One-Class Classification
RotNet + Translation + Self-Attention + Resize
85.7
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
RotNet + Translation + Self-Attention
84.8
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
RotNet + Self-Attention
81.6
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
RotNet + Translation
77.9
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
RotNet
65.3
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Supervised (OE)
56.1
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
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HyperAI
HyperAI
الرئيسية
المنصة
الوثائق
الأخبار
الأوراق البحثية
Notebooks
مجموعات البيانات
الموسوعة
SOTA
نماذج LLM
لوحة الأداء GPU
الفعاليات
البحث
حول
شروط الخدمة
سياسة الخصوصية
العربية
HyperAI
HyperAI
Toggle Sidebar
البحث في الموقع...
⌘
K
Command Palette
Search for a command to run...
Console
تسجيل الدخول
الرئيسية
SOTA
كشف الأخطاء
Anomaly Detection On One Class Imagenet 30
Anomaly Detection On One Class Imagenet 30
المقاييس
AUROC
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار المرجعي
Columns
اسم النموذج
AUROC
عنوان الورقة
Code
BCE-Clip (OE)
99.90
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
CLIP (Zero Shot)
99.88
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
Binary Cross Entropy (OE)
97.7
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
CSI
91.6
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
FCDD
91
Explainable Deep One-Class Classification
RotNet + Translation + Self-Attention + Resize
85.7
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
RotNet + Translation + Self-Attention
84.8
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
RotNet + Self-Attention
81.6
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
RotNet + Translation
77.9
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
RotNet
65.3
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Supervised (OE)
56.1
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
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