https://github.com/HilbertXu/MAML-Tensorflow
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
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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.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Bag of Tricks for Image Classification with Convolutional Neural Networks
SNIP: Single-shot Network Pruning based on Connection Sensitivity
Scene Clustering Based Pseudo-labeling Strategy for Multi-modal Aerial View Object Classification
Relation Network for Multi-label Aerial Image Classification
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Efficient Image Dataset Classification Difficulty Estimation for Predicting Deep-Learning Accuracy
Long short-term memory and learning-to-learn in networks of spiking neurons
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.