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Related Concept Videos

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

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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...
154
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

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The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
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Three-Winding Transformers01:19

Three-Winding Transformers

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Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
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Energy Losses in Transformers01:21

Energy Losses in Transformers

875
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
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Types Of Transformers01:16

Types Of Transformers

976
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...
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Transformers01:26

Transformers

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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
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  1. Home
  3. Are Transformer-based Models More Robust Than Cnn-based Models?
  1. Home
  3. Are Transformer-based Models More Robust Than Cnn-based Models?

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Are transformer-based models more robust than CNN-based models?

Zhendong Liu1, Shuwei Qian1, Changhong Xia1

  • 1Nanjing University, 163 Xianlin Avenue, Qixia District, Nanjing, Jiangsu 210023, China.

Neural Networks : the Official Journal of the International Neural Network Society
|January 24, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Transformer models show superior robustness compared to CNNs in real-world AI applications. This study introduces new metrics and methods to improve deep learning model robustness against data corruption.

Keywords:
Data augmentationDeep learningImage classificationModel robustness

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Area of Science:

  • Artificial Intelligence
  • Deep Learning
  • Computer Vision

Background:

  • The increasing deployment of AI models necessitates robust performance in open environments.
  • Evaluating the robustness of deep learning models, especially transformers and CNNs, is critical.
  • Existing robustness metrics may not fully capture real-world performance.

Purpose of the Study:

  • To compare the robustness of transformer-based and CNN-based deep learning models.
  • To identify the sources of robustness from structural and process perspectives.
  • To develop improved evaluation metrics and enhancement methods for AI model robustness.

Main Methods:

  • Comparative analysis of transformer and CNN models on robustness metrics.
  • Investigation of robustness through Fourier transform and game interaction analysis.
  • Development of a calibrated evaluation metric and a blur-based enhancement method.

    • Main Results:

    • Transformer models generally exhibit better robustness than CNN models across various metrics.
    • Analysis revealed insights into the underlying mechanisms of transformer robustness.
    • The proposed calibrated metric and blur-based method achieved state-of-the-art results.

    Conclusions:

    • Transformer models offer enhanced robustness for AI deployment.
    • New evaluation and enhancement strategies can significantly improve model resilience.
    • Achieved state-of-the-art performance on benchmark datasets (CIFAR-10-C, CIFAR-100-C, TinyImageNet-C).