Yes, CNNs can be more robust than transformers. CNNs are better at generalizing from data and they are less likely to overfit. Additionally, CNNs can be trained on more data faster than transformers.
Can CNNs be more robust than transformers?
The answer to this question is not a simple one. There are pros and cons to each approach and it really depends on the task at hand as to which approach is better.
CNNs have been shown to be more robust than transformers when it comes to classification tasks. This is because CNNs are able to learn feature hierarchies, which allows them to better generalize to new data.
However, transformers have been shown to be more robust than CNNs when it comes to language tasks.
This is because transformers are able to capture long-range dependencies, which is something that CNNs struggle with.
Ultimately, it is up to the user to decide which approach is best for their specific task.
AI2: Safety and Robustness Certification of Neural Networks
Early convolutions help transformers see better
In machine learning, convolutional neural networks (ConvNets or CNNs) are a class of deep, feed-forward artificial neural networks, most commonly applied to analyzing visual imagery.
Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex. Individual cortical neurons respond to stimuli only in a small region of the visual field known as the receptive field.
The receptive fields of different neurons overlap such that they cover the entire visual field.
Each convolutional layer in a ConvNet consists of a set of learnable filters (kernels), with each filter spanning all channels in the input. During the forward pass, the kernels are convolved with the input to produce a set of output feature maps.
The number of output feature maps is equal to the number of kernels.
The output of a convolutional layer is often passed through a non-linear activation function, such as a rectified linear unit (ReLU).
The primary advantage of ConvNets over other types of neural networks is that they require much less pre-processing.
That is, the input to a ConvNet is typically an image that does not need to be flattened into a vector. This means that the ConvNet can learn to recognize patterns of pixels in an image, without having to learn explicit patterns of pixel relationships.
Credit: deepai.org
Are Transformers more robust than CNNs Openreview?
Yes, Transformers are more robust than CNNs. Here’s why:
Transformers are able to handle more variable input sizes than CNNs.
This is because Transformers use an attention mechanism, which allows them to focus on specific parts of the input regardless of its size.
Transformers also have a higher capacity for modeling long-range dependencies than CNNs. This is because Transformers are not limited by the fixed size of convolutional kernels.
Overall, Transformers are more flexible and powerful than CNNs, and thus can be more robust to changes in data or input size.
Are CNNs or Transformers more like human vision?
If you ask most people which is more like human vision, they will probably say CNNs. After all, CNNs are the most common type of neural network used for image classification, and they have been around for much longer than transformers.
However, if you ask a computer vision expert, they will likely say that transformers are more like human vision.
This is because transformers are able to capture global features, whereas CNNs only capture local features.
To understand the difference, let’s take a look at how each type of neural network works.
A CNN is a type of neural network that is designed to work with images.
It does this by taking an image and breaking it down into a series of smaller images, or “patches.” Each patch is then processed by a separate neuron in the CNN. The CNN then looks for patterns in the patches and uses these patterns to classify the image.
A transformer is also a type of neural network that is designed to work with images. However, unlike a CNN, a transformer does not break an image down into patches. Instead, it processes the entire image at once.
This allows it to capture global features, such as the overall shape of an object.
So, which is more like human vision?
Well, it depends on how you look at it.
If you consider how a human processes an image, then a transformer is more like human vision.
Why are CNNs better?
There are many reasons why Convolutional Neural Networks (CNNs) are better than traditional neural networks. First, CNNs are designed to work with data that has a spatial structure, such as images. This is because CNNs are built using a series of convolutional layers, which extract features from images by sliding a filter over the image and taking the dot product of the filter and the image.
This results in a feature map, which is then fed into a pooling layer. Pooling layers downsample the feature map, which helps to reduce the computational complexity of the CNN and also makes the CNN more robust to small changes in the input data.
Second, CNNs are able to learn complex patterns in data.
This is because CNNs are composed of multiple layers, each of which can learn increasingly complex patterns. For example, the first layer of a CNN might learn to detect edges in an image, while the second layer might learn to detect shapes, and the third layer might learn to detect objects.
Third, CNNs are efficient at generalizing from data.
This is because CNNs learn features that are local and translation invariant. That is, features that are learned in one part of the image are also likely to be present in other parts of the image. This makes CNNs very good at recognizing objects in images, even if they are not perfectly aligned.
Fourth, CNNs are easy to train.
Should we replace CNNs with Transformers for medical images?
There is no easy answer when it comes to deciding whether or not to replace CNNs with Transformers for medical images. Both types of models have their own strengths and weaknesses, so it really depends on the specific application and what kind of results you are hoping to achieve.
That being said, Transformers have shown some promise in medical image applications.
For example, they have been used to successfully identify melanoma skin cancer. Additionally, Transformers are often better at handling complex data sets, so they may be a better choice for more intricate medical image analysis.
Ultimately, the decision of which type of model to use depends on the individual circumstances.
If you are working with a large and complex data set, a Transformers may be the better choice. However, if you are looking for more straightforward results, a CNN may be a better option.
Conclusion
Yes, CNNs can be more robust than transformers. CNNs are less likely to overfit on training data and have better generalization performance. Additionally, CNNs are less sensitive to changes in the input data and can learn from smaller datasets.