Results 41 to 50 of about 142,324 (226)

Learning from Noisy Labels with Distillation

, 2017
The ability of learning from noisy labels is very useful in many visual recognition tasks, as a vast amount of data with noisy labels are relatively easy to obtain. Traditionally, the label noises have been treated as statistical outliers, and approaches
Cao, Liangliang, Li, Li-Jia, Li, Yuncheng, Luo, Jiebo, Song, Yale, Yang, Jianchao   +5 more
core   +1 more source

Intermolecular Interactions as Driving Force of Increasing Multiphoton Absorption in a Perylene Diimide‐Based Coordination Polymer

Advanced Functional Materials, EarlyView.
This study uncovers the unexplored role of intermolecular interactions in multiphoton absorption in coordination polymers. By analyzing [Zn2tpda(DMA)2(DMF)0.3], it shows how the electronic coupling of the chromophores and confinement in the MOF enhance two‐and three‐photon absorption.
Simon Nicolas Deger, Anna Mauri, Yang Cui, Sebastian Josef Weishäupl, Ali Deniz Özdemir, Hamad Syed, Aleksandr Ovsianikov, Wolfgang Wenzel, Alexander Pöthig, Jürgen Hauer, Mariana Kozlowska, Roland Augustinus Fischer   +11 more
wiley   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

NTCE-KD: Non-Target-Class-Enhanced Knowledge Distillation

Sensors
Most logit-based knowledge distillation methods transfer soft labels from the teacher model to the student model via Kullback–Leibler divergence based on softmax, an exponential normalization function. However, this exponential nature of softmax tends to
Chuan Li, Xiao Teng, Yan Ding, Long Lan
doaj   +1 more source

Transformer-Based Knowledge Distillation with Ghost Attention for Multimodal Edge-Based Smart Surveillance [PDF]

ITM Web of Conferences
In the modern era, knowledge distillation has gained attention as an important technique for edge-based smart surveillance that integrates accurate yet lightweight deployable models on resource-constrained devices.
Sataar Zahrah
doaj   +1 more source

Chemoselective Sequential Polymerization: An Approach Toward Mixed Plastic Waste Recycling

Advanced Functional Materials, EarlyView.
Inspired by biological protein metabolism, this study demonstrates the closed‐loop recycling of mixed synthetic polymers via ring‐closing depolymerization followed by a chemoselective sequential polymerizations process. The approach recovers pure polymers from mixed feedstocks, even in multilayer formats, highlighting a promising strategy to overcome a
Gadi Slor, Youwei Ma, Hyunwoo Park, Tae‐Lim Choi, Damien Guironnet, Francesco Stellacci   +5 more
wiley   +1 more source

Knowledge distillation of face recognition via attention cosine similarity review

IET Computer Vision
Deep learning‐based face recognition models have demonstrated remarkable performance in benchmark tests, and knowledge distillation technology has been frequently accustomed to obtain high‐precision real‐time face recognition models specifically designed
Zhuo Wang, SuWen Zhao, WanYi Guo
doaj   +1 more source

Distilling Diverse Knowledge for Deep Ensemble Learning

IEEE Access
Bidirectional knowledge distillation improves network performance by sharing knowledge between networks during the training of multiple networks. Additionally, performance is further improved by using an ensemble of multiple networks during inference ...
Naoki Okamoto, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi   +3 more
doaj   +1 more source

Named Entity Recognition Model Based on k-best Viterbi Decoupling Knowledge Distillation [PDF]

Jisuanji kexue yu tansuo
Knowledge distillation is a general approach to improve the performance of the named entity recognition (NER) models. However, the classical knowledge distillation loss functions are coupled, which leads to poor logit distillation.
ZHAO Honglei, TANG Huanling, ZHANG Yu, SUN Xueyuan, LU Mingyu
doaj   +1 more source

SI‐bioATRP in Mesoporous Silica for Size‐Exclusion Driven Local Polymer Placement

Advanced Functional Materials, EarlyView.
An enzyme‐catalyzed surface‐initiated atom transfer radical polymerization (SI‐bioATRP) of an anionic monomer within mesoporous silica particles, using hemoglobin as a catalyst, allows for controlling the location of the formed polymer via size‐exclusion effects between the nanopores and the biomacromolecules, thereby opening routes to functional ...
Oleksandr Wondra, Eleonora Ornati, Robert Lehn, Joanna J. Mikolei, Hergen Breitzke, Raheleh Pardehkhorram, Gerd Buntkowsky, Nico Bruns, Annette Andrieu‐Brunsen   +8 more
wiley   +1 more source