25 citations · 27 across the 5 of their papers we have counts for
5 papers
Condensed Data Expansion Using Model Inversion for Knowledge Distillation
Kuluhan Binici, Shivam Aggarwal, Cihan Acar +4
Condensed datasets offer a compact representation of larger datasets, but training models directly on them or using them to enhance model performance through knowledge distillation…
Robust and Resource-Efficient Data-Free Knowledge Distillation by Generative Pseudo Replay
Kuluhan Binici, Shivam Aggarwal, Nam Trung Pham +2
Data-Free Knowledge Distillation (KD) allows knowledge transfer from a trained neural network (teacher) to a more compact one (student) in the absence of original training data. Ex…
Preventing Catastrophic Forgetting and Distribution Mismatch in Knowledge Distillation via Synthetic Data
Kuluhan Binici, Nam Trung Pham, Tulika Mitra +1
With the increasing popularity of deep learning on edge devices, compressing large neural networks to meet the hardware requirements of resource-constrained devices became a signif…
Feature-less Stitching of Cylindrical Tunnel
Ramanpreet Singh Pahwa, Wei Kiat Leong, Shaohui Foong +2
Traditional image stitching algorithms use transforms such as homography to combine different views of a scene. They usually work well when the scene is planar or when the camera i…
Shadow Optimization from Structured Deep Edge Detection
Li Shen, Teck Wee Chua, Karianto Leman
Local structures of shadow boundaries as well as complex interactions of image regions remain largely unexploited by previous shadow detection approaches. In this paper, we present…