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Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

943
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
943
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

223
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
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Transformers in Distribution System01:27

Transformers in Distribution System

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
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Convolution Properties I01:20

Convolution Properties I

131
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
131
Convolution Properties II01:17

Convolution Properties II

166
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
166
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

129
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
129

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Demystify Transformers & Convolutions in Modern Image Deep Networks.

Xiaowei Hu, Min Shi, Weiyun Wang

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    Vision transformers benefit from advanced architectures, not just feature designs. This study identifies spatial token mixers (STMs) as key differentiators, revealing their impact on performance and robustness.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Vision transformers (ViTs) have emerged as powerful backbones in computer vision, offering performance improvements.
    • Advancements in ViTs are attributed to both novel feature transformation designs and sophisticated network/block architectures.

    Purpose of the Study:

    • To isolate and quantify the performance gains from different spatial token mixers (STMs) used in vision transformers.
    • To provide a fair comparison of convolution and attention operators by neutralizing architectural variations.

    Main Methods:

    • Introduced a unified architecture to standardize network and block-level designs.
    • Integrated and evaluated various spatial token mixers (STMs) within this unified framework.
    • Conducted experiments across diverse tasks and analyzed inductive biases.

    Main Results:

    • Advanced network and block-level designs significantly boost performance.
    • Distinct performance differences were observed among various STMs.
    • Analysis revealed insights into STMs' effective receptive fields, invariance properties, and adversarial robustness.

    Conclusions:

    • Spatial token mixers (STMs) are critical for vision transformer performance, with variations impacting results.
    • Network and block-level architectural choices play a substantial role in overall performance gains.
    • The study provides a framework for understanding and optimizing STMs in vision transformer design.