Interpolation
الاستيفاء
تقنية لتقدير قيم بين نقاط بيانات معروفة. في سياق mixup يعني إنشاء عيّنات تدريبية جديدة كمزيج خطي لعيّنتين موجودتين ومزج تصنيفاتهما بالنسبة ذاتها.
A technique for estimating values between known data points. In the mixup context it means creating new training examples as a linear blend of two existing ones and mixing their labels in the same proportion.
Also translated asالاستكمال الداخلي، الاستيفاء الخطي
First appears in this corpus in: Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation (2014)
Appears in these papers
- Denoising Diffusion Implicit Models2021in the sky ✦
- Denoising Diffusion Implicit Models2021in the sky ✦
- Reconciling Modern Machine-Learning Practice and the Classical Bias–Variance Trade-Off2019in the sky ✦
- Reconciling Modern Machine-Learning Practice and the Classical Bias–Variance Trade-Off2019in the sky ✦
- Flow Matching for Generative Modeling2023in the sky ✦
- Flow Matching for Generative Modeling2023in the sky ✦
- mixup: Beyond Empirical Risk Minimization2018in the sky ✦
- mixup: Beyond Empirical Risk Minimization2018in the sky ✦
- Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation2014in the sky ✦
- A Style-Based Generator Architecture for Generative Adversarial Networks2019in the sky ✦
- A Style-Based Generator Architecture for Generative Adversarial Networks2019in the sky ✦