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[HTML][HTML] Data augmentation: A comprehensive survey of modern approaches
A Mumuni, F Mumuni - Array, 2022 - Elsevier
To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …
A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations
Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …
massive model sizes that require significant computational and storage resources. To …
Structured pruning for deep convolutional neural networks: A survey
The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …
attributed to their deeper and wider architectures, which can come with significant …
A review of convolutional neural network architectures and their optimizations
The research advances concerning the typical architectures of convolutional neural
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …
Transformer in transformer
Transformer is a new kind of neural architecture which encodes the input data as powerful
features via the attention mechanism. Basically, the visual transformers first divide the input …
features via the attention mechanism. Basically, the visual transformers first divide the input …
Distilling object detectors via decoupled features
Abstract Knowledge distillation is a widely used paradigm for inheriting information from a
complicated teacher network to a compact student network and maintaining the strong …
complicated teacher network to a compact student network and maintaining the strong …
Patch slimming for efficient vision transformers
This paper studies the efficiency problem for visual transformers by excavating redundant
calculation in given networks. The recent transformer architecture has demonstrated its …
calculation in given networks. The recent transformer architecture has demonstrated its …
Chip: Channel independence-based pruning for compact neural networks
Filter pruning has been widely used for neural network compression because of its enabled
practical acceleration. To date, most of the existing filter pruning works explore the …
practical acceleration. To date, most of the existing filter pruning works explore the …
A review of artificial intelligence in embedded systems
Z Zhang, J Li - Micromachines, 2023 - mdpi.com
Advancements in artificial intelligence algorithms and models, along with embedded device
support, have resulted in the issue of high energy consumption and poor compatibility when …
support, have resulted in the issue of high energy consumption and poor compatibility when …
Sparser spiking activity can be better: Feature refine-and-mask spiking neural network for event-based visual recognition
Event-based visual, a new visual paradigm with bio-inspired dynamic perception and μ s
level temporal resolution, has prominent advantages in many specific visual scenarios and …
level temporal resolution, has prominent advantages in many specific visual scenarios and …