Supervised ImageNet

Supervised ImageNet research focuses on improving image classification models by leveraging the massive ImageNet dataset. Current efforts concentrate on enhancing data curation strategies, developing more efficient training methods (including exploring alternative architectures like binary neural networks and leveraging self-supervised learning), and addressing challenges like dataset bias and the need for explainable AI. These advancements are crucial for improving the accuracy, efficiency, and trustworthiness of computer vision systems across various applications, from medical imaging to agricultural technology.

Papers

February 10, 2023

February 9, 2023

Towards Geospatial Foundation Models via Continual Pretraining
Matias Mendieta, Boran Han, Xingjian Shi, Yi Zhu, Chen Chen
Supervised ImageNet Pre Trained Representation Continual Pre Training Geospatial Foundation Model Large Scale ImageNet

February 3, 2023

Self-Supervised In-Domain Representation Learning for Remote Sensing Image Scene Classification
Ali Ghanbarzade, Hossein Soleimani
Supervised ImageNet Remote Sensing Image Self Supervised Pre Training Land Cover Self Supervised Learning Method Remote Sensing Scene Classification

January 31, 2023

NASiam: Efficient Representation Learning using Neural Architecture Search for Siamese Networks
Alexandre Heuillet, Hedi Tabia, Hichem Arioui
Representation Learning Neural Architecture Search Supervised ImageNet Siamese Network Self Supervised Visual Representation Novel Deep

January 29, 2023

January 25, 2023

Self-Supervised Curricular Deep Learning for Chest X-Ray Image Classification
Iván de Andrés Tamé, Kirill Sirotkin, Pablo Carballeira, Marcos Escudero-Viñolo
Supervised ImageNet Chest X Ray Curriculum Learning Medical Image Data Pneumonia Detection Supervised Training

January 23, 2023

A Simple Recipe for Competitive Low-compute Self supervised Vision Models
Quentin Duval, Ishan Misra, Nicolas Ballas
Knowledge Distillation Self Supervised Supervised ImageNet Vision Model Joint Embedding Optimal Model Simple Recipe Supervised Knowledge Distillation

January 20, 2023

Holistically Explainable Vision Transformers
Moritz Böhle, Mario Fritz, Bernt Schiele
Vision Transformer Supervised ImageNet Attention Layer Attention Module B Co

January 19, 2023

Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, Nicolas Ballas
Vision Transformer Self Supervised Learning Supervised ImageNet Semantic Representation Visual Semantic Joint Embedding Predictive Architecture

January 17, 2023

Learning Customized Visual Models with Retrieval-Augmented Knowledge
Haotian Liu, Kilho Son, Jianwei Yang, Ce Liu, Jianfeng Gao, Yong Jae Lee, Chunyuan Li
Contrastive Learning Supervised ImageNet Image Text Pair Retrieval Augmented Visual Model Zero Shot Classification Task Personalized REtrieval

January 15, 2023

Adaptive Deep Neural Network Inference Optimization with EENet
Fatih Ilhan, Ka-Ho Chow, Sihao Hu, Tiansheng Huang, Selim Tekin, Wenqi Wei, Yanzhao Wu, Myungjin Lee, Ramana Kompella, Hugo Latapie, Gaowen Liu, Ling Liu
Deep Neural Network Supervised ImageNet DNN Model DNN Inference Deep Learning Inference Multi Exit

January 13, 2023

Learnable Heterogeneous Convolution: Learning both topology and strength
Rongzhen Zhao, Zhenzhi Wu, Qikun Zhang
Supervised ImageNet Estimated Team Strength Topology Problem Convolution Technique Heterogeneous Dynamic Dendritic Like Structure

January 11, 2023

Does progress on ImageNet transfer to real-world datasets?
Alex Fang, Simon Kornblith, Ludwig Schmidt
Pre Trained Model Supervised ImageNet Real World Datasets Image Classification Datasets ImageNet Accuracy Imagenet Transfer

January 3, 2023

A New Perspective to Boost Vision Transformer for Medical Image Classification
Yuexiang Li, Yawen Huang, Nanjun He, Kai Ma, Yefeng Zheng
Vision Transformer Self Supervised Learning Supervised ImageNet Self Supervised Representation Learning Medical Image Classification New Perspective