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Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
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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.
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Learning Efficient Convolutional Networks through Network Slimming
Bag of Tricks for Image Classification with Convolutional Neural Networks
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Deep Object Co-segmentation via Spatial-Semantic Network Modulation
KeepAugment: A Simple Information-Preserving Data Augmentation Approach
Aggregated Residual Transformations for Deep Neural Networks
Very Deep Convolutional Networks for Large-Scale Image Recognition
A Simple Framework for Contrastive Learning of Visual Representations
A Simple Framework for Contrastive Learning of Visual Representations
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
Confident Learning: Estimating Uncertainty in Dataset Labels
Learning Transferable Visual Models From Natural Language Supervision
Model-Agnostic Meta-Learning for Fast Adaptation of Deep 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.