5 citations · 9 across the 4 of their papers we have counts for
7 papers
HRank: Filter Pruning using High-Rank Feature Map
Mingbao Lin, Rongrong Ji, Yan Wang +4
Neural network pruning offers a promising prospect to facilitate deploying deep neural networks on resource-limited devices. However, existing methods are still challenged by the t…
Channel Pruning via Automatic Structure Search
Mingbao Lin, Rongrong Ji, Yuxin Zhang +3
Channel pruning is among the predominant approaches to compress deep neural networks. To this end, most existing pruning methods focus on selecting channels (filters) by importance…
Hadamard Codebook Based Deep Hashing
Shen Chen, Liujuan Cao, Mingbao Lin +5
As an approximate nearest neighbor search technique, hashing has been widely applied in large-scale image retrieval due to its excellent efficiency. Most supervised deep hashing me…
Supervised Online Hashing via Similarity Distribution Learning
Mingbao Lin, Rongrong Ji, Shen Chen +6
Online hashing has attracted extensive research attention when facing streaming data. Most online hashing methods, learning binary codes based on pairwise similarities of training…
Supervised Online Hashing via Hadamard Codebook Learning
Mingbao Lin, Rongrong Ji, Hong Liu +1
In recent years, binary code learning, a.k.a hashing, has received extensive attention in large-scale multimedia retrieval. It aims to encode high-dimensional data points to binary…
Hadamard Matrix Guided Online Hashing
Mingbao Lin, Rongrong Ji, Hong Liu +3
Online image hashing has attracted increasing research attention recently, which receives large-scale data in a streaming manner to update the hash functions on-the-fly. Its key ch…