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Related Concept Videos

Active Filters01:25

Active Filters

1.0K
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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Passive Filters01:27

Passive Filters

719
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...
719
Associative Learning01:27

Associative Learning

686
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
686
Introduction to Learning01:18

Introduction to Learning

614
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
614
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.0K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.0K
Purposive Learning01:22

Purposive Learning

239
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
239

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Related Experiment Videos

Semantically Adversarial Learnable Filters.

Ali Shahin Shamsabadi, Changjae Oh, Andrea Cavallaro

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |September 20, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new adversarial framework to create image perturbations that fool classifiers. The method uses image content and label semantics to generate robust and transferable adversarial examples.

    Related Experiment Videos

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Adversarial attacks pose a significant threat to the reliability of image classifiers.
    • Existing methods often lack robustness and transferability across different models.

    Purpose of the Study:

    • To develop an adversarial framework for generating image perturbations that effectively mislead classifiers.
    • To enhance the robustness and transferability of adversarial attacks.

    Main Methods:

    • A multi-task objective function combining structure loss and semantic adversarial loss was employed.
    • A fully convolutional neural network was trained to generate perturbations guided by image processing filters and label semantics.
    • The framework was validated using detail enhancement, log transformation, and gamma correction filters.

    Main Results:

    • The proposed framework successfully generated adversarial perturbations with high success rates.
    • Adversarially filtered images demonstrated robustness against targeted classifiers.
    • The generated perturbations showed significant transferability to unseen classifiers.

    Conclusions:

    • The developed adversarial framework is effective in creating robust and transferable image perturbations.
    • The approach offers a novel way to assess and improve the security of image classification systems.