Deep Model Merging: The Sister of Neural Network Interpretability -- A Survey

TL;DR

This survey explores how loss landscape geometry influences neural network training, interpretability, and robustness, highlighting key characteristics like mode convexity and connectivity, and proposing future research directions.

Contribution

It synthesizes empirical findings on model merging and loss landscapes, connecting them to interpretability and robustness, and suggests new research avenues.

Findings

Identifies four key loss landscape characteristics: mode convexity, determinism, directedness, connectivity.

Links model merging insights to interpretability and robustness.

Proposes new research directions at the intersection of these fields.

Abstract

We survey the model merging literature through the lens of loss landscape geometry to connect observations from empirical studies on model merging and loss landscape analysis to phenomena that govern neural network training and the emergence of their inner representations. We distill repeated empirical observations from the literature in these fields into descriptions of four major characteristics of loss landscape geometry: mode convexity, determinism, directedness, and connectivity. We argue that insights into the structure of learned representations from model merging have applications to model interpretability and robustness, subsequently we propose promising new research directions at the intersection of these fields.