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

January 29, 2023

Diverse, Difficult, and Odd Instances (D2O): A New Test Set for Object Classification
Ali Borji
Supervised ImageNet Object Recognition Diverse Set Object Classification Test Set ImageNet E

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

December 16, 2022

December 15, 2022

On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practices
Salah Ghamizi, Maxime Cordy, Michail Papadakis, Yves Le Traon
Deep Neural Network Adversarial Attack Native Robustness Adversarial Robustness Supervised ImageNet Common Pitfall Best Practice Chest X Ray Classification

December 12, 2022

December 10, 2022

Algorithmic progress in computer vision
Ege Erdil, Tamay Besiroglu
Computer Vision Supervised ImageNet Algorithmic Progress