Multi Task, Multi Modal less than 1 minute read Multi Task & Multi Modal Single Task : Input X —> Output Y (ex. Object Classification) Multi Task : Input X —> Output Y1, Y2, Y3 (ex. Object Detection) Multi Modal : Input X1, X2, X3 —> Output Y (ex. 동영상(음성, 영상) 입력을 통한 예측 ) Twitter Facebook LinkedIn Previous Next
RemixMatch, FixMatch less than 1 minute read Semi-Supervised Learning Supervised Learning은 Labeled data만을 이용하여 만들어진다. But, 현실세계에서는 Unlabeled data가 훨씬 많고 Labeled data와 Unlabeled data를 같이 학습시키는 것이 Se...
Deep Compression less than 1 minute read Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale less than 1 minute read An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale - Vision Transformer(ViT) Method
Auto-ML less than 1 minute read Auto ML - Machine Learning 모델 학습에서 가장 중요한 것은 올바른 Data의 확보 그리고 Hyperparameter Tunning 일 것이다. Auto-ML은 이 Hyperparameter Tunning을 자동으로 해주는 방법이다.
