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Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
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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.
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Designing Neural Network Architectures using Reinforcement Learning
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Designing Neural Network Architectures using Reinforcement Learning
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks
Multiscale Principle of Relevant Information for Hyperspectral Image Classification
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
Very Deep Convolutional Networks for Large-Scale Image Recognition
X3D: Expanding Architectures for Efficient Video Recognition
A Simple Framework for Contrastive Learning of Visual Representations
Bootstrap your own latent: A new approach to self-supervised Learning
Towards Deep Learning Models Resistant to Adversarial Attacks
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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.