Structure your answer around the core axes: inductive biases, data/compute efficiency, long-range dependency handling, and typical use cases. Then discuss how hybrid models like Convolutional Vision Transformers (CvT) or Swin Transformers combine convolutional and attention mechanisms to balance efficiency and expressiveness. Emphasize trade-offs and practical considerations for real-world deployment, especially in resource-constrained environments like mobile at Snapchat.
Pro tip: Relate the discussion to Snapchat's use cases: on-device CNNs for real-time AR filters and transformers for multimodal content understanding, highlighting how hybrid models can optimize latency and accuracy. Mention that the choice depends on the specific task, data scale, and hardware constraints.
Explain that CNNs have strong inductive biases like locality, translation equivariance, and weight sharing, while transformers have minimal inductive biases and rely on data to learn spatial relationships.
Discuss that CNNs are more sample-efficient and require less compute, while transformers need large datasets and significant compute (e.g., GPUs/TPUs) for training, though they scale well with data.
Contrast CNNs' limited receptive fields (requiring stacking layers to capture global context) with transformers' self-attention that directly models long-range dependencies in a single layer.
Give examples: CNNs excel in image classification, object detection, and on-device vision; transformers dominate NLP, and are increasingly used in vision (ViT) and multimodal tasks.
Describe how hybrids like Swin Transformer, CvT, or Convolutional Vision Transformers combine convolutional layers for local feature extraction with attention for global context, achieving better accuracy/efficiency trade-offs.
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