Glossary

0-9

1-shot learning 2-stage detector 3D convolution 4D data 5G + AI 6DoF pose estimation 7D representation 8-bit quantization 9-layer network 0-shot learning

A

AGI / Artificial General Intelligence Artificial Intelligence (AI)Attention Algorithm Autoencoder

B

BERT Boosting Bias Backpropagation Batch Normalization

C

Cross-Validation Classifier / Classification CNN / Convolutional Neural Network Chatbot Clustering

D

Deepfake Discriminative Model Data Augmentation Deep Learning Deterministic Model

E

Epoch Explainable AI (XAI)Embedding Encoder Ensemble Learning

F

Forward Propagation Foundation Model Fine-tuning Feature Extraction Fusion / Multimodal Fusion

G

GAN / Generative Adversarial Network Grounding Graph Neural Network (GNN)Gradient Descent Generative AI

H

Hierarchical Model Hallucination Heuristic Hyperparameter Hidden Layer

I

Instruction tuning Imbalanced Data Instance / Sample Intelligence Amplification / Augmentation Interpretability

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

K-Shot Learning Kernel Trick KL Divergence (Kullback–Leibler Divergence)K-means Clustering Knowledge Distillation

L

LSTM / Long Short-Term Memory Large Language Model (LLM)Learning Rate Loss Function Latent Variable

M

Meta-learning Multimodal / Multimodality Multi-head Attention Model Machine Learning (ML)

N

NLU / Natural Language Understanding Novelty Detection / Anomaly Detection Normalization Neural Network NLP / Natural Language Processing

O

One-hot Encoding Optimizer Objective Function Online Learning Overfitting

P

Prompt Pooling Parameter Policy / Reinforcement Learning Policy Pretraining

Q

Q-learning Quality Estimation Query Quantization Queue / Buffer

R

RNN / Recurrent Neural Network Reinforcement Learning (RL)Retrieval Augmented Generation (RAG)Regularization Representation Learning

S

Softmax Sampling Sequence Modeling Supervised Learning Self-Supervised Learning

T

Tokenizer Transformer Training Data Tuning / Hyperparameter Tuning Transfer Learning

U

U-Net Uncertainty Estimation Underfitting Unsupervised Learning Universal Approximation Theorem

V

Vision Transformer (ViT)Vector Embedding Vanishing / Exploding Gradient Variational Autoencoder (VAE)Validation Set

W

Weight Decay Workflow Weak Supervision Whitening / Whitening Transformation Word Embedding

X

X-axis / feature axis XLM XLNet XAI / Explainable AI XOR problem

Y

YAGNI (You Aren't Gonna Need It)Yoga of AI Y-transform / YUV Y-axis / feature axis Yield (model yield / throughput)

Z

Z-score Normalization Zygosity in augmentation Zero-centric / Zero-bias initialization Zero-gradient phenomenon Zero-shot Learning / Zero-shot inference

什麼是 U-Net

U-Net 是一種用於圖像分割的深度學習架構，最初在醫學圖像處理領域取得了顯著成就。其架構靈感來源於傳統的卷積神經網絡（CNN），具有對稱的編碼器-解碼器結構。U-Net 的一個顯著特點是每一步解碼過程中引入來自編碼器的特徵圖，這大大提高了分割的精度。

U-Net 的重要性在於其能夠在相對較少的訓練樣本下，保持高效的性能，尤其是在醫學領域，數據標註成本高昂且樣本稀缺。U-Net 的運作方式是通過逐層的卷積和池化操作提取圖像特徵，最後通過上採樣和卷積恢復圖像的空間分辨率。

在實際應用中，U-Net 被廣泛應用於細胞分割、醫學影像分析和遙感圖像處理等各種圖像分割任務。隨著深度學習技術的不斷發展，U-Net 也在不斷演進，出現了許多變種和改進版本，如 Attention U-Net 和 3D U-Net，以適應不同的應用需求。

未來，U-Net 和其變種將在高維數據處理和複雜場景分割中繼續發揮重要作用。隨著計算能力的增強和數據集的豐富，U-Net 的應用範圍預計將進一步擴展。

其優點包括高效性、較少的訓練樣本需求和良好的分割精度，而缺點在於對於非常複雜的圖像，可能需要額外的改進和調整。