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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 Encoder

An 'Encoder' is a device or program that converts information or data from one format to another. It is primarily used in computer science and engineering to transform raw data into a more efficient format for storage or transmission, such as encoding audio, video, or text data into a digital format.


The operation of an encoder typically involves analyzing input data and generating corresponding encoded output. Different types of encoders use various algorithms and protocols. For example, video encoders may use standards like H.264, HEVC, or VP9, while audio encoders might use MP3, AAC, or FLAC. The process often includes compressing data to reduce file size while maintaining the quality of the original data.


Encoders are widely applied in various scenarios, including streaming services like Netflix or YouTube, video conferencing software such as Zoom or Microsoft Teams, and digital audio playback like Spotify or Apple Music. In these contexts, encoders ensure that audio and video content can be transmitted efficiently and presented with optimal quality.


With the evolution of technology, the performance and efficiency of encoders are continuously improving. The application of artificial intelligence and machine learning is driving advancements in encoders, enabling them to process data compression and format conversion more intelligently. Furthermore, as 8K video and high dynamic range (HDR) content become more prevalent, the demand and complexity of encoders are increasing.


The main advantages of encoders are their ability to effectively reduce data size, saving storage space and bandwidth. However, the encoding process can lead to some degree of information loss, especially when using lossy compression algorithms. Additionally, certain encoders may require high computational resources, resulting in delays during encoding and decoding.


When choosing an encoder, users should consider their specific needs, including the desired compression ratio, output quality, and real-time capability. Understanding the compatibility and standards of different encoders is also crucial to ensure smooth playback across various devices and platforms.