https://github.com/PaddlePaddle/PASSL/blob/main/docs/Train_BYOL_model.md
Bootstrap your own latent: A new approach to self-supervised Learning
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Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Bootstrap your own latent: A new approach to self-supervised Learning
Learning Transferable Visual Models From Natural Language Supervision
Momentum Contrast for Unsupervised Visual Representation Learning
Training data-efficient image transformers & distillation through attention
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Context Autoencoder for Self-Supervised Representation Learning
Context Autoencoder for Self-Supervised Representation Learning
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Context Autoencoder for Self-Supervised Representation Learning
Bag of Tricks for Image Classification with Convolutional Neural Networks
Momentum Contrast for Unsupervised Visual Representation Learning
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
Momentum Contrast for Unsupervised Visual Representation Learning
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations
Transfusion: Understanding Transfer Learning for Medical Imaging
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.