Jack and Masters of all Trades: One-Pass Learning Sets of Model Sets From Large Pre-Trained Models

TL;DR

This paper introduces a novel neuroevolutionary method to efficiently create diverse sets of compact, specialized models that collectively handle multiple tasks and environmental conditions, addressing limitations of large pre-trained models.

Contribution

It presents the first one-pass neuroevolutionary approach to generate a set of specialized models, enabling multi-task and multi-environment adaptability in a resource-efficient manner.

Findings

Successful creation of diverse model sets in a single evolutionary pass

Enhanced multi-task performance with compact models

Potential for resource-constrained environments

Abstract

For deep learning, size is power. Massive neural nets trained on broad data for a spectrum of tasks are at the forefront of artificial intelligence. These large pre-trained models or Jacks of All Trades (JATs), when fine-tuned for downstream tasks, are gaining importance in driving deep learning advancements. However, environments with tight resource constraints, changing objectives and intentions, or varied task requirements, could limit the real-world utility of a singular JAT. Hence, in tandem with current trends towards building increasingly large JATs, this paper conducts an initial exploration into concepts underlying the creation of a diverse set of compact machine learning model sets. Composed of many smaller and specialized models, the Set of Sets is formulated to simultaneously fulfil many task settings and environmental conditions. A means to arrive at such a set tractably in one pass of a neuroevolutionary multitasking algorithm is presented for the first time, bringing us closer to models that are collectively Masters of All Trades.