Start by clarifying the goal: define 'interesting' or 'novel' in operational terms, then propose a scalable pipeline that combines unsupervised learning (e.g., clustering, density estimation, or self-supervised embeddings) with a lightweight human-in-the-loop validation. Emphasize trade-offs between exploration (finding truly novel images) and exploitation (mining known interesting categories), and discuss how to minimize human labeling through active learning and weak supervision.
Pro tip: Frame the problem as a ranking task: the system should output a prioritized list of candidate images for human review, not a binary classification. This allows you to use precision@k or recall@k as metrics and naturally incorporates human feedback to iteratively improve the ranking model.
Clarify what 'interesting' or 'novel' means for the application (e.g., rare objects, unusual compositions, aesthetic quality) and choose measurable proxies such as embedding-space distance, cluster rarity, or classifier uncertainty. Define success metrics like precision@k of human-rated interesting images.
Use self-supervised learning (e.g., contrastive learning, masked autoencoders) on the unlabeled dataset to obtain compact, informative embeddings. Alternatively, leverage a pre-trained model (e.g., CLIP) and fine-tune if needed. This representation enables efficient similarity search and clustering at scale.
Apply clustering (e.g., k-means on embeddings) or density estimation to identify rare or diverse samples. Use techniques like coreset selection, outlier detection, or uncertainty sampling to propose a small set of candidates that are likely to be interesting. Ensure diversity by sampling from different clusters or using determinantal point processes.
Present the top candidates to human labelers for binary or pairwise judgments (e.g., 'interesting' vs 'not'). Use active learning to select the most informative samples for labeling, minimizing human effort. Train a lightweight classifier or ranking model on these labels to generalize and improve future candidate selection.
Deploy the pipeline in a loop: mine candidates, get human feedback, update the model, and repeat. Monitor for drift and ensure the system remains scalable by using approximate nearest neighbor search and distributed processing. Evaluate trade-offs between exploration (novelty) and exploitation (known interesting categories).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.