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Glossary

0-9
0-shot learning1-shot learning2-stage detector3D convolution4D data5G + AI6DoF pose estimation7D representation8-bit quantization9-layer network
A
AGI / Artificial General IntelligenceAlgorithmArtificial Intelligence (AI)AttentionAutoencoder
B
BackpropagationBatch NormalizationBERTBiasBoosting
C
ChatbotClassifier / ClassificationClusteringCNN / Convolutional Neural NetworkCross-Validation
D
Data AugmentationDeep LearningDeepfakeDeterministic ModelDiscriminative Model
E
EmbeddingEncoderEnsemble LearningEpochExplainable AI (XAI)
F
Feature ExtractionFine-tuningForward PropagationFoundation ModelFusion / Multimodal Fusion
G
GAN / Generative Adversarial NetworkGenerative AIGradient DescentGraph Neural Network (GNN)Grounding
H
HallucinationHeuristicHidden LayerHierarchical ModelHyperparameter
I
Imbalanced DataInstance / SampleInstruction tuningIntelligence Amplification / AugmentationInterpretability
J
JAXJitteringJoint EmbeddingJSONL / JSON-linesJuxtaposition
K
K-means ClusteringK-Shot LearningKernel TrickKL Divergence (Kullback–Leibler Divergence)Knowledge Distillation
L
Large Language Model (LLM)Latent VariableLearning RateLoss FunctionLSTM / Long Short-Term Memory
M
Machine Learning (ML)Meta-learningModelMulti-head AttentionMultimodal / Multimodality
N
Neural NetworkNLP / Natural Language ProcessingNLU / Natural Language UnderstandingNormalizationNovelty Detection / Anomaly Detection
O
Objective FunctionOne-hot EncodingOnline LearningOptimizerOverfitting
P
ParameterPolicy / Reinforcement Learning PolicyPoolingPretrainingPrompt
Q
Q-learningQuality EstimationQuantizationQueryQueue / Buffer
R
RegularizationReinforcement Learning (RL)Representation LearningRetrieval Augmented Generation (RAG)RNN / Recurrent Neural Network
S
SamplingSelf-Supervised LearningSequence ModelingSoftmaxSupervised Learning
T
TokenizerTraining DataTransfer LearningTransformerTuning / Hyperparameter Tuning
U
U-NetUncertainty EstimationUnderfittingUniversal Approximation TheoremUnsupervised Learning
V
Validation SetVanishing / Exploding GradientVariational Autoencoder (VAE)Vector EmbeddingVision Transformer (ViT)
W
Weak SupervisionWeight DecayWhitening / Whitening TransformationWord EmbeddingWorkflow
X
X-axis / feature axisXAI / Explainable AIXLMXLNetXOR problem
Y
Y-axis / feature axisY-transform / YUVYAGNI (You Aren't Gonna Need It)Yield (model yield / throughput)Yoga of AI
Z
Z-score NormalizationZero-centric / Zero-bias initializationZero-gradient phenomenonZero-shot Learning / Zero-shot inferenceZygosity in augmentation

What is Data Augmentation

Data augmentation is a technique used to increase the diversity of training datasets, especially in machine learning and deep learning. By applying transformations such as rotation, scaling, cropping, and adding noise to existing samples, new samples can be generated, enhancing the model's generalization ability and reducing overfitting.


The importance of data augmentation is multifaceted. In situations where data is scarce, it effectively increases the amount of data available for training, improving the model's performance. Additionally, by introducing diversity, augmented samples help the model learn key features better, thus enhancing its performance on unseen samples.


In terms of operation, data augmentation techniques can be categorized into several types, including geometric transformations, color transformations, and noise injection. Geometric transformations like rotation and flipping can change the perspective of images; color transformations adjust brightness and contrast, altering the color distribution of images; noise injection adds random noise to images, improving the model's robustness to imperfect data.


Typical applications can be found in image recognition, natural language processing, and audio analysis. For instance, in image recognition, rotating and cropping images generates more training samples, thereby improving the accuracy of classification models. In natural language processing, techniques such as synonym replacement and sentence reordering can augment text data.


The future trend of data augmentation may lean towards more automated and intelligent approaches, such as using Generative Adversarial Networks (GANs) to produce high-quality augmented samples. Moreover, with the rise of self-supervised learning, data augmentation will likely be more closely integrated with unsupervised learning methods.


Despite its significant advantages in enhancing model performance, data augmentation also has drawbacks. Inappropriate augmentation may introduce erroneous samples, leading to reduced model performance. Furthermore, excessive data augmentation might result in the model learning unnecessary features, adversely affecting its performance on real data. Therefore, it is crucial to carefully choose suitable augmentation strategies and conduct proper evaluations.