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

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

B

BERT Boosting Backpropagation Batch Normalization Bias

C

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

D

Discriminative Model Deterministic Model Deep Learning Data Augmentation Deepfake

E

Epoch Encoder Ensemble Learning Explainable AI (XAI)Embedding

F

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

G

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

H

Hierarchical Model Hyperparameter Hallucination Heuristic Hidden Layer

I

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

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

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

L

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

M

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

N

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

O

One-hot Encoding Overfitting Objective Function Optimizer Online Learning

P

Prompt Parameter Pretraining Policy / Reinforcement Learning Policy Pooling

Q

Q-learning Query Queue / Buffer Quantization Quality Estimation

R

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

S

Softmax Sampling Supervised Learning Sequence Modeling Self-Supervised Learning

T

Tokenizer Transformer Tuning / Hyperparameter Tuning Transfer Learning Training Data

U

U-Net Uncertainty Estimation Underfitting Universal Approximation Theorem Unsupervised Learning

V

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

W

Weight Decay Word Embedding Workflow Whitening / Whitening Transformation Weak Supervision

X

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

Y

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

Z

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

什么是卷积神经网络（CNN）

卷积神经网络（CNN）是一种深度学习模型，特别在图像识别和处理方面非常有效。其基本理念是模仿人类视觉系统，通过多个卷积层逐步提取图像特征。CNN 最早由 Yann LeCun 在 1980 年代提出，并在 2012 年的 ImageNet 竞赛中取得显著成功，极大地推动了深度学习的研究与应用。

CNN 通常由输入层、多个卷积层、池化层、全连接层和输出层组成。卷积层通过卷积操作提取局部特征，而池化层则降低特征的维度，从而减少计算复杂度，同时保留重要信息。经过多次卷积和池化，最终特征通过全连接层映射到输出标签。

CNN 被广泛应用于计算机视觉任务，如图像分类、目标检测和图像分割等。例如，谷歌的 Inception 模型和 Facebook 的 Mask R-CNN 都是基于 CNN 的成功案例。它们在医学影像分析、自主驾驶和视频监控等领域的应用也日益增多。

随着数据量的激增和计算能力的提升，CNN 的应用领域将不断扩展。边缘计算、增强现实和虚拟现实等新兴技术的发展也将推动 CNN 的进一步创新。此外，结合生成对抗网络（GAN）等技术，CNN 可能在生成模型方面取得新的突破。

尽管 CNN 在处理图像数据时表现出色，但它们也存在一些局限性，如对大规模数据集的需求和对计算资源的高消耗。此外，模型的可解释性问题仍然是研究的热点。在使用 CNN 时，需要充分对数据进行预处理，以提高模型的准确性。