The retrieval plus ranking framing is the interesting part here.
Start by clarifying requirements (e.g., latency, accuracy, on-device vs. cloud) and then outline a two-stage system: first retrieve candidate leaf categories using an embedding-based nearest neighbor search, then re-rank them with a lightweight model. Emphasize trade-offs between retrieval efficiency and ranking accuracy, and discuss how to handle scalability and user experience.
Pro tip: Mention that the retrieval step can be done on-device using a compressed embedding model to reduce latency and privacy concerns, while the ranking model can be a small neural network that runs efficiently on mobile. This shows awareness of practical deployment constraints.
Ask about expected latency, accuracy targets, device constraints (on-device vs. cloud), and the number of leaf categories. This ensures the design aligns with real-world needs.
Propose using a pre-trained image encoder (e.g., MobileNet) to generate embeddings for the query leaf image, then perform approximate nearest neighbor search (e.g., using FAISS or ScaNN) over a database of leaf embeddings to retrieve top-K candidates.
Use a lightweight ranking model (e.g., a small MLP or gradient boosted trees) that takes the query embedding and candidate embeddings (or additional features) to re-rank the top-K candidates and output the final category.
Explain how to train the embedding model (e.g., using triplet loss or contrastive learning) and the ranking model (e.g., using pairwise or listwise ranking losses). Mention data augmentation and handling of new categories.
Talk about on-device vs. cloud inference, model quantization, latency vs. accuracy trade-offs, and how to update the index as new leaf categories are added.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.