What are some of the pitfalls of using deep learning in vision?

Deep learning has become a common tool for vision tasks, but it comes with several practical challenges. First, deep learning models require large amounts of labeled data to perform well. For example, training an object detection system like YOLO or Faster R-CNN typically demands thousands of annotated images, which can be expensive and time-consuming to collect. In medical imaging, where expert annotations are costly, this becomes a significant barrier. Even when data is available, imbalances—like rare objects in a dataset—can lead to models that perform poorly on underrepresented cases. A model trained to detect tumors might miss subtle cases if they weren’t sufficiently represented in the training data.

Another major issue is computational cost. Training vision models often requires powerful GPUs or TPUs, which can be inaccessible for small teams or individual developers. For instance, training a high-resolution image generator like StyleGAN from scratch might take weeks on multiple GPUs, costing thousands of dollars in cloud compute. Additionally, deploying these models in real-world applications can be tricky. A model that works well on a server might struggle on mobile devices due to memory or latency constraints. Developers often have to compress models using techniques like quantization or pruning, which can reduce accuracy. For example, converting a ResNet model to run efficiently on a smartphone camera might cut its precision by 5-10%, impacting reliability.

Finally, deep learning models for vision tasks can be brittle and hard to interpret. They might fail unpredictably on inputs that differ slightly from training data—a problem known as “distribution shift.” A self-driving car’s vision system trained in sunny weather might struggle in fog, even if humans can adapt. Adversarial attacks, where tiny pixel-level changes fool a model (e.g., making a stop sign unrecognizable), also highlight this fragility. Additionally, debugging model errors is difficult because the internal decision-making process is opaque. If a facial recognition system misclassifies a person, it’s nearly impossible to trace why without specialized tools like saliency maps, which only provide limited insight. This lack of transparency raises ethical and safety concerns in critical applications like surveillance or healthcare.