What are the advantages and disadvantages of using convolutional neural networks for image recognition?

Because CNNs have an inherent bias with respect to the structure of 2D data it can learn with fewer examples compared to some vision transformer architectures. Sometimes fully connected neural networks can be replaced with convolution blocks resulting in speedup and sparsification. 1x1 convolutions are very helpful in enabling compression in neural network weights

I agree but it also has the following disadvantages: High computational requirements: CNNs are computationally expensive to train and run. This is because they have a large number of parameters that need to be updated during training. Additionally, CNNs are often used with large datasets, which can further increase the computational requirements. Need for large datasets: CNNs typically require large datasets to train effectively. This is because they learn to recognize patterns in data by analyzing many examples of those patterns. If the dataset is too small, the CNN may not be able to learn the patterns effectively and may perform poorly on new data.

While CNNs indeed require large labeled datasets and are often seen as black boxes, there are strategies to mitigate these issues. Transfer Learning can be a solution for the data hunger. By using a pre-trained CNN model on a related task, you can fine-tune it for your specific image recognition problem with a much smaller dataset, reducing both time and annotation costs. For the 'black box' issue, Model Explainability tools are emerging that break down the decision-making process of complex models. Techniques like LIME/SHAP can provide insights into which features are contributing most to a given prediction, making CNNs more interpretable. These don't eliminate the inherent disadvantages but can make CNNs more accessible and trustworthy.

Although very powerful tool and scale/parallelize better than RNNs, the choice of CNN structure (number of layers, activation functions, normalization, where to place skip-layer connections, or simply use existing models with transfer learning), all of these make the hyperparameter-space exceedingly large. Possible combination with genetic type algorithm or Monte-Carlo parameter sampling cloud optimize the starting conditions. Another issue, is related to image size (most models are pretrained on small image size) using pooling type technics lead to loss of information, geometry (cartesian vs polar symmetry).

In addition to RNNs, CapsNets, and GANs, there are also other methods like ensemble learning, transfer learning, and graph neural networks that can complement or compete with CNNs for image recognition, each offering unique advantages and applications in various scenarios. These diverse approaches provide a rich toolkit for researchers and practitioners to explore and leverage in the field of computer vision.

Sometimes convolutional neural networks (CNNs) fit the task, sometimes support vector machines (SVMs) are better. Sometimes we should not be using AI algorithms because there may be a better non-AI way to do it. There are many tools in the toolbox. I find that it is all about choosing the right tool to do the right job, given the data that is being processed and the application that is being targeted. And the knowledge of choosing which tool to use to do the right job comes from experience.