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Related Concept Videos

Passive Filters01:27

Passive Filters

1.0K
Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
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Active Filters01:25

Active Filters

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Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
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Long-term Depression01:05

Long-term Depression

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Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
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Primary and Secondary Growth in Roots and Shoots03:02

Primary and Secondary Growth in Roots and Shoots

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Vascular plants, which account for over 90% of the Earth’s vegetation, all undergo primary growth—which lengthens roots and shoots. Many land plants, notably woody plants, also undergo secondary growth—which thickens roots and shoots.
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Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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Meristems and Plant Growth02:36

Meristems and Plant Growth

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Plants grow throughout their lives; this is called indeterminate growth, and it distinguishes plants from most animals. Although certain parts of plants stop growing (e.g., leaves and flowers), others grow continuously—like roots and stems.
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ThiNet: Pruning CNN Filters for a Thinner Net.

Jian-Hao Luo, Hao Zhang, Hong-Yu Zhou

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    |July 25, 2018
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    Summary
    This summary is machine-generated.

    ThiNet, a novel framework for deep neural network compression, effectively prunes filters for efficient deployment on mobile devices. This method achieves state-of-the-art results, compressing VGG-16 to 2.66 MB while maintaining accuracy.

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    Area of Science:

    • Computer Vision
    • Deep Learning
    • Model Compression

    Background:

    • Deep neural networks (DNNs) are computationally intensive, limiting their deployment on resource-constrained devices.
    • Existing model compression techniques often struggle to balance efficiency and accuracy.

    Purpose of the Study:

    • To develop an effective and unified framework for accelerating and compressing Convolutional Neural Network (CNN) models.
    • To enable the deployment of DNNs on small devices like mobile phones and embedded systems.

    Main Methods:

    • Proposed ThiNet (Thin Net) framework for filter-level pruning, discarding unimportant filters.
    • Formulated filter pruning as an optimization problem, using statistics from the next layer for pruning decisions.
    • Introduced 'gcos' (Group Convolution with Shuffling) for further model size reduction.

    Main Results:

    • Demonstrated state-of-the-art effectiveness in model compression.
    • Compressed the VGG-16 model to 2.66 MB (ThiNet-Tiny) while preserving AlexNet-level accuracy.
    • Achieved excellent generalization ability across various vision tasks including classification, detection, and segmentation.

    Conclusions:

    • ThiNet offers a powerful approach for compressing DNNs, making them suitable for edge devices.
    • The proposed pruning strategy and 'gcos' scheme significantly reduce model size without compromising performance.
    • The compressed models exhibit strong performance and adaptability to diverse computer vision applications.