https://github.com/akwasigroch/Pretext-Invariant-Representations
Self-Supervised Learning of Pretext-Invariant Representations
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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.
Self-Supervised Learning of Pretext-Invariant Representations
Xception: Deep Learning with Depthwise Separable Convolutions
Very Deep Convolutional Networks for Large-Scale Image Recognition
Improved Regularization of Convolutional Neural Networks with Cutout
Presence-Only Geographical Priors for Fine-Grained Image Classification
Presence-Only Geographical Priors for Fine-Grained Image Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
Training data-efficient image transformers & distillation through attention
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
A Simple Framework for Contrastive Learning of Visual Representations
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Momentum Contrast for Unsupervised Visual Representation Learning
Verification of Deep Convolutional Neural Networks Using ImageStars
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.