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研0卑微 主要方向是机器学习-计算机视觉,求一个入门学习路线,前期应该看哪些入门的论文


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  • herosunly Python领域优质创作者 2022-09-21 17:44


    • 2010年:Noise-contrastive Estimation: a New Estimation Principle for Unnormalized Statistical Models
    • 2012年:ImageNet Classification with Deep Convolutional Neural Networks
    • 2013年:Visualizing and Understanding Convolutional Networks
    • 2015年
      • Very Deep Convolutional Networks for Large-Scale Image Recognition
      • Going Deeper with Convolutions
      • FaceNet: a Unified Embedding for Face Recognition and Clustering
    • 2016年
      • Rethinking the Inception Architecture for Computer Vision
      • Deep Residual Learning for Image Recognition
      • Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
    • 2017年:Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
    • 2018年
      • From Recognition to Cognition: Visual Commonsense Reasoning
      • Focal Loss for Dense Object Detection
      • Relational Inductive Biases, Deep Learning, and Graph Networks
    • 2019年
      • Objects As Points
      • RandAugment: Practical Automated Data Augmentation with a Reduced Search Space
      • Semantic Image Synthesis with Spatially-Adaptive Normalization
    • 2020年
      • Denoising Diffusion Probabilistic Models
      • Designing Network Design Spaces
      • An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
      • Training Data-efficient Image Transformers & Distillation Through Attention
      • NeRF: Representing Scenes As Neural Radiance Fields for View Synthesis
      • Bootstrap Your Own Latent: a New Approach to Self-supervised Learning
      • A Simple Framework for Contrastive Learning of Visual Representations
      • Conditional Negative Sampling for Contrastive Learning of Visual Representations
      • Momentum Contrast for Unsupervised Visual Representation Learning
      • Generative Pretraining from Pixels
    • 2021年
      • Do Vision Transformers See Like Convolutional Neural Networks?
      • BEiT: BERT Pre-Training of Image Transformers
      • Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows
      • RepVGG: Making VGG-style ConvNets Great Again
      • An Empirical Study of Training Self-Supervised Vision Transformers
      • Diffusion Models Beat GANs on Image Synthesis
    • 2022年
      • A ConvNet for the 2020s
      • Natural Language Descriptions of Deep Visual Features
      • Vision Models are More Robust and Fair When Pretrained on Uncurated Images Without Supervision
      • Block-NeRF: Scalable Large Scene Neural View Synthesis
      • VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
      • Masked Autoencoders are Scalable Vision Learners
      • The Effects of Regularization and Data Augmentation are Class Dependent
      • Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
      • Pix2seq: a Language Modeling Framework for Object Detection
      • An Improved One Millisecond Mobile Backbone
      • Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
      • Swin Transformer V2: Scaling up Capacity and Resolution
      • Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
    本回答被题主选为最佳回答 , 对您是否有帮助呢?



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