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1-shot学习

1-shot学习是一种机器学习方法，专注于如何在仅有一个训练样本的情况下进行有效的分类。这种方法在数据稀缺的场景中尤其有用，例如图像识别和自然语言处理。与传统的深度学习模型需要大量数据进行训练不同，1-shot学习通过利用先前的知识和迁移学习，使模型能够从有限的样本中学习并进行准确的预测。

1-shot学习的重要性在于它能够显著降低数据收集和标注的成本，特别是在医疗影像和安防监控等领域，获取大量标注数据往往困难且昂贵。通过这种学习方法，模型能够迅速适应新任务，减少训练时间和资源消耗。

这种学习技术通常包括生成特征嵌入，并使用距离度量（如欧几里得距离或余弦相似度）来判断新样本与已知样本之间的相似性。常用的技术包括Siamese网络、原型网络和度量学习等。

典型的应用场景包括人脸识别、物体检测和语音识别。例如，在人脸识别中，系统只需提供一张新面孔的图片，就能准确识别该人。未来，1-shot学习的发展趋势将朝着更高的准确率和更广泛的应用领域，尤其是在实时处理和在线学习方面。

然而，1-shot学习也存在局限性，模型可能对样本的选择非常敏感，且在样本质量较低的情况下，学习效果可能不佳。因此，应用时需要谨慎选择样本，并考虑数据的多样性。