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 Algorithm Autoencoder Artificial Intelligence (AI)Attention

B

BERT Backpropagation Batch Normalization Boosting Bias

C

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

D

Deepfake Deep Learning Data Augmentation Deterministic Model Discriminative Model

E

Epoch Ensemble Learning Encoder Explainable AI (XAI)Embedding

F

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

G

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

H

Hyperparameter Heuristic Hidden Layer Hierarchical Model Hallucination

I

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

J

JAX JSONL / JSON-lines Jittering Joint Embedding Juxtaposition

K

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

L

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

M

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

N

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

O

One-hot Encoding Overfitting Optimizer Online Learning Objective Function

P

Pooling Parameter Pretraining Prompt Policy / Reinforcement Learning Policy

Q

Q-learning Queue / Buffer Query Quality Estimation Quantization

R

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

S

Softmax Sampling Sequence Modeling Supervised Learning Self-Supervised Learning

T

Tuning / Hyperparameter Tuning Tokenizer Transformer Training Data Transfer Learning

U

U-Net Underfitting Universal Approximation Theorem Unsupervised Learning Uncertainty Estimation

V

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

W

Weight Decay Whitening / Whitening Transformation Workflow Word Embedding Weak Supervision

X

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

Y

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

Z

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

3D畳み込みとは何か

3D畳み込みは、従来の畳み込み操作の拡張であり、画像や動画処理に広く使用されている深層学習の一部です。2D畳み込みとは異なり、3D畳み込みは幅、高さ、深さの3次元で操作を行い、空間的および時間的な特徴を効果的に捉えることができます。

この手法は、医療画像処理、動画分析、3D再構成などの3次元データを扱う際に特に重要です。医療画像処理では、3D畳み込みがCTやMRIスキャンを分析し、診断精度を向上させるのに役立ちます。動画処理では、3D畳み込みが動きのパターンを認識し、監視や行動認識に貢献します。

しかし、3D畳み込みはより包括的な特徴を捉えるなど多くの利点がある一方で、計算複雑性が高く、リソース要件が2D畳み込みよりも大きいため、欠点も存在します。

今後、計算能力の向上と深層学習技術の進化に伴い、3D畳み込みの応用はさらに広がると期待されており、関連技術の進展を促進するでしょう。