Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching

Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching

29 May 2023

Shengyuan Pang

Papers citing "Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching"

7 / 7 papers shown

Title
Loss-Curvature Matching for Dataset Selection and Condensation Seung-Jae Shin Heesun Bae DongHyeok Shin Weonyoung Joo Il-Chul Moon DD 38 24 0 08 Mar 2023
Graph Condensation via Receptive Field Distribution Matching Mengyang Liu Shanchuan Li Xinshi Chen Le Song DD 69 32 0 28 Jun 2022
Dataset Condensation via Efficient Synthetic-Data Parameterization Jang-Hyun Kim Jinuk Kim Seong Joon Oh Sangdoo Yun Hwanjun Song Joonhyun Jeong Jung-Woo Ha Hyun Oh Song DD 375 155 0 30 May 2022
One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective Jiun Tian Hoe KamWoh Ng Tianyu Zhang Chee Seng Chan Yi-Zhe Song Tao Xiang MQ 20 83 0 29 Sep 2021
Dataset Condensation with Differentiable Siamese Augmentation Bo-Lu Zhao Hakan Bilen DD 183 219 0 16 Feb 2021
Densely Connected Convolutional Networks Gao Huang Zhuang Liu L. V. D. van der Maaten Kilian Q. Weinberger PINN 3DV 236 35,884 0 25 Aug 2016
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 279 39,083 0 01 Sep 2014