Computer vision faces challenges with data dependency. Many models require large, high-quality datasets for training, which may not always be available or diverse enough to handle real-world scenarios. Bias in datasets can lead to poor performance in identifying underrepresented groups or objects. Another limitation is computational cost. Training and deploying computer vision models, especially deep learning-based ones, demand significant computational power and storage. This can limit accessibility for smaller organizations or resource-constrained devices like edge systems. Generalization remains a hurdle. Models often struggle when exposed to environments or conditions different from their training data. For instance, an object detection model trained in sunny weather may fail in foggy conditions, posing challenges for applications like autonomous driving.
What are the current major limitations of computer vision?
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What impact does the metric have on performance? For instance, is computing cosine similarity generally more or less efficient than Euclidean, or is it roughly the same after transformations?
The computational efficiency of cosine similarity versus Euclidean distance depends on whether vectors are normalized an
Can data augmentation be applied to structured data?
Yes, data augmentation can be applied to structured data, although it is more commonly associated with unstructured data
Can neural networks explain their predictions?
Neural networks struggle to explain their predictions directly because they are often considered "black-box" models. The