Deep Model Merging: The Sister of Neural Network Interpretability -- A Survey
This is an incremental survey paper that synthesizes existing research for researchers in neural network interpretability and model merging.
This survey paper analyzes model merging literature through loss landscape geometry to identify four key characteristics (mode convexity, determinism, directedness, connectivity) that connect empirical observations about neural network training and representations. It argues these insights can enhance model interpretability and robustness while proposing new research directions.
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.