사진: Unsplash의 Marcus Loke
딥러닝 모델에서 One-shot learning과 Few-shot learning은 모델을 학습하는 데 사용되는 데이터의 양을 나타내는 용어입니다. 이 용어들은 특히 이미지 인식, 자연어 처리, 게임 등의 분야에서 사용됩니다.
One-shot learning
하나의 샘플 이미지만으로 새로운 클래스를 인식하는 것을 의미합니다. 예를 들어, 고양이를 인식하는 딥러닝 모델에게 "스핑크스"라는 새로운 종류의 고양이를 인식하도록 요청한다면, 모델은 이전에 본 적이 없는 스핑크스 고양이 사진 하나만으로도 인식을 수행할 수 있어야 합니다. 이것은 학습 데이터가 매우 제한적인 경우에 유용합니다.
Few-shot learning
한 클래스당 일부 샘플 이미지만 사용하여 새로운 클래스를 인식하는 것을 의미합니다. 예를 들어, 알파벳 손글씨 이미지를 분류하는 딥러닝 모델에게 "종이 상자"라는 클래스를 추가하라는 요청을 받았다면, 모델은 종이 상자에 대한 몇 가지 샘플 이미지를 보고 이를 인식할 수 있어야 합니다. 이것은 one-shot learning보다는 학습 데이터가 더 많이 필요하지만, 전체 데이터셋을 학습하는 것보다 효율적인 방법입니다.
딥러닝 모델이 one-shot 및 few-shot learning을 수행하려면 몇 가지 기본 개념을 이해해야 합니다. 이러한 개념에는 다음과 같은 것들이 있습니다.
- 특징 추출 (Feature extraction): 딥러닝 모델이 이미지나 텍스트 등의 데이터를 분석하고 이해하기 위해 사용하는 고차원 특징 데이터를 추출하는 과정입니다.
- 유사도 측정 (Similarity measurement): 새로운 데이터와 기존의 학습 데이터 간의 유사도를 측정하여 새로운 데이터가 어느 클래스에 속하는지 결정합니다.
- 분류 알고리즘 (Classification algorithm): 유사도 측정 결과를 기반으로 새로운 데이터를 올바른 클래스로 분류하는 알고리즘입니다. 이러한 알고리즘에는 k-최근접 이웃(k-NN), 서포트 벡터 머신(SVM), 신경망 등이 있습니다.
또한 이러한 학습을 위해 사용되는 기술에는 신경망 아키텍처의 변형 및 데이터 증강 기술이 포함됩니다.
- 신경망 아키텍처 변형: 일반적인 딥러닝 모델보다 더 적은 수의 학습 데이터로도 학습이 가능하도록 하는 신경망 아키텍처 변형이 필요합니다. 대표적인 예로는 신경망의 가중치를 공유하는 시각적 추론 네트워크(Siamese Network)와 유사도를 학습하는 메타-학습 모델(Meta-Learning Model) 등이 있습니다.
- 데이터 증강: 적은 수의 학습 데이터로도 학습 성능을 향상시키기 위해, 기존의 학습 데이터를 변형하거나 학습 데이터에 노이즈를 추가하는 등의 방식으로 데이터를 증강합니다. 이를 통해 모델이 데이터의 다양성을 더 잘 이해하도록 돕습니다.
이러한 기술들은 one-shot 및 few-shot 학습을 수행하는 데 매우 중요합니다. 이러한 학습 방법은 현실 세계에서 실용적인 응용 분야에서 매우 유용합니다. 예를 들어, 군대나 의료 분야에서 적은 수의 샘플 이미지를 사용하여 새로운 클래스를 식별하는 것이 중요할 수 있습니다.
Zero-shot learning (ZSL)
자연어 처리에서 제로샷 학습(Zero-shot learning)이란, 라벨링 되지 않은 새로운 클래스에 대한 분류 작업을 수행할 때, 이전에 학습된 모델을 사용하여 분류하는 기술입니다.
기존의 지도 학습에서는 학습 데이터에 포함된 클래스에 대해서만 분류 작업을 수행할 수 있습니다. 하지만 제로샷 학습은 이전에 학습된 모델을 사용하여 학습 데이터에 없는 새로운 클래스를 인식하고 분류할 수 있습니다. 이를 위해서는 새로운 클래스에 대한 설명 정보를 입력으로 주어야 합니다.
예를 들어, '강아지', '고양이', '토끼' 등의 동물 분류 모델이 있다고 가정해보겠습니다. 이 모델이 '사자'라는 클래스에 대해서는 학습되지 않았지만, '사자'라는 동물의 특징을 설명하는 텍스트 정보를 입력하면, 모델은 이를 이용하여 '사자'를 분류할 수 있습니다.
제로샷 학습은 일반적으로 큰 양의 라벨링된 데이터가 없는 상황에서 유용하게 사용됩니다. 예를 들어, 특정 산업 분야나 문제에 대한 전문 지식이 필요한 경우, 이전에 학습된 모델을 사용하여 새로운 문제를 해결하는 데 도움을 줄 수 있습니다.
Reference
- ChatGPT
- Wikipedia - One-shot learning (computer vision)
- A Guide to One-Shot Learning
- Wikipedia - Few-shot learning (natural language processing)
- Wikipedia - Zero-shot learning
- Hugging Face - Zero-Shot Classification
- Papers with Code - Zero-Shot Learning
- Wikipedia - Meta-learning (computer science)
- Wikipedia - Siamese neural network
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