Loss Functions and Metrics in Deep Learning

TL;DR

This paper provides a comprehensive review of loss functions and metrics in deep learning, offering a unified framework, discussing multi-loss setups, and analyzing specialized metrics for modern applications to guide effective model training and evaluation.

Contribution

It introduces a unified framework for understanding losses and metrics, discusses multi-loss strategies, and offers new insights into evaluation metrics for complex deep learning tasks.

Findings

Unified framework for losses and metrics

Insights into multi-loss training strategies

Analysis of metrics for retrieval-augmented generation

Abstract

This paper presents a comprehensive review of loss functions and performance metrics in deep learning, highlighting key developments and practical insights across diverse application areas. We begin by outlining fundamental considerations in classic tasks such as regression and classification, then extend our analysis to specialized domains like computer vision and natural language processing including retrieval-augmented generation. In each setting, we systematically examine how different loss functions and evaluation metrics can be paired to address task-specific challenges such as class imbalance, outliers, and sequence-level optimization. Key contributions of this work include: (1) a unified framework for understanding how losses and metrics align with different learning objectives, (2) an in-depth discussion of multi-loss setups that balance competing goals, and (3) new insights into specialized metrics used to evaluate modern applications like retrieval-augmented generation, where faithfulness and context relevance are pivotal. Along the way, we highlight best practices for selecting or combining losses and metrics based on empirical behaviors and domain constraints. Finally, we identify open problems and promising directions, including the automation of loss-function search and the development of robust, interpretable evaluation measures for increasingly complex deep learning tasks. Our review aims to equip researchers and practitioners with clearer guidance in designing effective training pipelines and reliable model assessments for a wide spectrum of real-world applications.