DeiT Explained in 3 Minutes! | Data Efficient Transformers скачать в хорошем качестве

DeiT Explained in 3 Minutes! | Data Efficient Transformers

Vision Transformers changed computer vision by replacing convolutions with attention. But there was a major problem: they required enormous datasets and huge computational resources to work properly. So how did researchers make transformers practical for real-world vision tasks? In this video, we explore DeiT (Data-Efficient Image Transformers), a breakthrough that showed transformers can be trained using only ImageNet while achieving performance competitive with convolutional neural networks. We cover: ✅ Why Vision Transformers needed massive datasets ✅ The idea behind data-efficient training ✅ Class token vs Distillation token ✅ Transformer-specific knowledge distillation ✅ Why CNNs are surprisingly better teachers ✅ Hard vs soft distillation explained intuitively ✅ Training tricks that made DeiT work ✅ How inductive bias is transferred through supervision DeiT is not just a new architecture. It is a new way of training transformers, and it played a key role in making modern vision transformers practical. 📄 Paper: Training Data-Efficient Image Transformers & Distillation through Attention 👨‍🔬 Authors: Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou #DeiT #VisionTransformer #Transformers #ComputerVision #DeepLearning #MachineLearning #AIResearch #KnowledgeDistillation #SelfAttention #ImageClassification #NeuralNetworks #AIExplained #CVPR #ArtificialIntelligence #DLResearch