Semantic Redundancies in Image-Classification Datasets: The 10% You Don't Need

29 January 2019

Papers citing "Semantic Redundancies in Image-Classification Datasets: The 10% You Don't Need"

10 / 10 papers shown

Title
Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation Thomas Kerdreux A. Tuel Quentin Febvre A. Mouche Bertrand Chapron 73 0 0 09 Apr 2025
An Empirical Comparison of Video Frame Sampling Methods for Multi-Modal RAG Retrieval Mahesh Kandhare Thibault Gisselbrecht 35 5 0 22 Jul 2024
DEFT: Data Efficient Fine-Tuning for Pre-Trained Language Models via Unsupervised Core-Set Selection Devleena Das Vivek Khetan 23 0 0 25 Oct 2023
Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset Ngoc Phuong Anh Duong Alexandre Almin Léo Lemarié B. R. Kiran 27 0 0 21 Feb 2023
LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation Ngoc Phuong Anh Duong Alexandre Almin Léo Lemarié B. R. Kiran 15 3 0 06 Feb 2022
How Low Can We Go? Pixel Annotation for Semantic Segmentation Daniel Kigli Ariel Shamir S. Avidan VLM 21 1 0 25 Jan 2022
On Training Instance Selection for Few-Shot Neural Text Generation Ernie Chang Xiaoyu Shen Hui-Syuan Yeh Vera Demberg 27 40 0 07 Jul 2021
Sequential Graph Convolutional Network for Active Learning Razvan Caramalau Binod Bhattarai Tae-Kyun Kim GNN 16 118 0 18 Jun 2020
Sampling Bias in Deep Active Classification: An Empirical Study Ameya Prabhu Charles Dognin M. Singh 11 64 0 20 Sep 2019
Imbalance Problems in Object Detection: A Review Kemal Oksuz Baris Can Cam Sinan Kalkan Emre Akbas ObjD 16 458 0 31 Aug 2019