https://github.com/MicPie/clasp
Contrastive Learning of Medical Visual Representations from Paired Images and Text
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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.
Contrastive Learning of Medical Visual Representations from Paired Images and Text
Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification
Automated Classification of Histopathology Images Using Transfer Learning
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
ISyNet: Convolutional Neural Networks design for AI accelerator
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
MnasNet: Platform-Aware Neural Architecture Search for Mobile
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer
MnasNet: Platform-Aware Neural Architecture Search for Mobile
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
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.