It's the Best Only When It Fits You Most: Finding Related Models for Serving Based on Dynamic Locality Sensitive Hashing
Lixi Zhou, Zijie Wang, Amitabh Das, Jia Zou

TL;DR
This paper introduces an efficient method for automatically finding related models for deployment by leveraging dataset similarity measures and locality sensitive hashing, reducing manual effort and computational costs.
Contribution
It proposes an adaptive similarity measurement based on Jensen-Shannon divergence combined with locality sensitive hashing for fast model retrieval.
Findings
The proposed JS divergence-based similarity is effective for model matching.
Locality sensitive hashing significantly accelerates similarity computation.
The method reduces manual effort in model selection for deployment.
Abstract
In recent, deep learning has become the most popular direction in machine learning and artificial intelligence. However, preparation of training data is often a bottleneck in the lifecycle of deploying a deep learning model for production or research. Reusing models for inferencing a dataset can greatly save the human costs required for training data creation. Although there exist a number of model sharing platform such as TensorFlow Hub, PyTorch Hub, DLHub, most of these systems require model uploaders to manually specify the details of each model and model downloaders to screen keyword search results for selecting a model. They are in lack of an automatic model searching tool. This paper proposes an end-to-end process of searching related models for serving based on the similarity of the target dataset and the training datasets of the available models. While there exist many…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Advanced Neural Network Applications · Domain Adaptation and Few-Shot Learning
