# Loss Functions and Metrics in Deep Learning

arXiv: https://arxiv.org/abs/2307.02694 · HTML: https://mlsanity.com/paper/2307.02694/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation.
- Builds on 9 of its 65 references that are in the map.
- First posted on arXiv 5 Jul 2023.

## Placed in

- Retrieval-augmented generation / [Generation and context integration](https://mlsanity.com/rag/generation-and-context-integration/)

## Builds on (most cited first, 9 of 9)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection](https://mlsanity.com/paper/1603.02199/) (2016)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [Online Learning: A Comprehensive Survey](https://mlsanity.com/paper/1802.02871/) (2018)
- [Brain Tumor Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1505.03540/) (2015)
- [Deep Learning on Image Denoising: An overview](https://mlsanity.com/paper/1912.13171/) (2019)
- [Neural Abstractive Text Summarization with Sequence-to-Sequence Models](https://mlsanity.com/paper/1812.02303/) (2018)

## Built on by (most cited first, 30 of 42)

- [Maximum Likelihood Reinforcement Learning](https://mlsanity.com/paper/2602.02710/) (2026)
- [A Pattern Language for Machine Learning Tasks](https://mlsanity.com/paper/2407.02424/) (2024)
- [Low-rank Optimization Trajectories Modeling for LLM RLVR Acceleration](https://mlsanity.com/paper/2604.11446/) (2026)
- [Bayesian Optimization for Hyperparameters Tuning in Neural Networks](https://mlsanity.com/paper/2410.21886/) (2024)
- [Introducing Background Temperature to Characterise Hidden Randomness in Large Language Models](https://mlsanity.com/paper/2604.22411/) (2026)
- [Noisy probing dose facilitated dose prediction for pencil beam scanning proton therapy: physics enhances generalizability](https://mlsanity.com/paper/2312.00975/) (2023)
- [Express Your Doubts -- Probabilistic World Modeling Should not be Based on Token logprobs](https://mlsanity.com/paper/2505.02072/) (2025)
- [Semimage: HSV-Based Semantic Image Encoding for Disentangled Text Representation](https://mlsanity.com/paper/2512.00088/) (2025)
- [Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey](https://mlsanity.com/paper/2601.22198/) (2026)
- [Grounding Hierarchical Vision-Language-Action Models Through Explicit Language-Action Alignment](https://mlsanity.com/paper/2604.05614/) (2026)
- [DIFF-ERO: A Conformance-Aware Loss for Deep Learning in Process Mining](https://mlsanity.com/paper/2606.14283/) (2026)
- [Enhancing Solar Driver Forecasting with Multivariate Transformers](https://mlsanity.com/paper/2406.15847/) (2024)
- [Generic Multimodal Spatially Graph Network for Spatially Embedded Network Representation Learning](https://mlsanity.com/paper/2502.00530/) (2025)
- [A Novel Loss Function for Deep Learning Based Daily Stock Trading System](https://mlsanity.com/paper/2502.17493/) (2025)
- [CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data](https://mlsanity.com/paper/2505.23879/) (2025)
- [Hybrid-Domain Synergistic Transformer for Hyperspectral Image Denoising](https://mlsanity.com/paper/2507.20099/) (2025)
- [Batch-CAM: Introduction to better reasoning in convolutional deep learning models](https://mlsanity.com/paper/2510.00664/) (2025)
- [Improving the accuracy and generalizability of molecular property regression models with a substructure-substitution-rule-informed framework](https://mlsanity.com/paper/2511.08314/) (2025)
- [Conversion rate prediction in online advertising: modeling techniques, performance evaluation and future directions](https://mlsanity.com/paper/2512.01171/) (2025)
- [WTNN: Weibull-Tailored Neural Networks for survival analysis](https://mlsanity.com/paper/2512.09163/) (2025)
- [BertsWin: Resolving Topological Sparsity in 3D Masked Autoencoders via Component-Balanced Structural Optimization](https://mlsanity.com/paper/2512.21769/) (2025)
- [From Preoperative CT to Postmastoidectomy Mesh Construction: Mastoidectomy Shape Prediction for Cochlear Implant Surgery](https://mlsanity.com/paper/2601.04405/) (2026)
- [VAMOS-OCTA: Vessel-Aware Multi-Axis Orthogonal Supervision for Inpainting Motion-Corrupted OCT Angiography Volumes](https://mlsanity.com/paper/2602.00995/) (2026)
- [Multi-head automated segmentation by incorporating detection head into the contextual layer neural network](https://mlsanity.com/paper/2602.02471/) (2026)
- [Don't stop me now: How Validation Criteria Affect Checkpoint Selection and Early Stopping](https://mlsanity.com/paper/2602.22107/) (2026)
- [Bi Directional Feedback Fusion for Activity Aware Forecasting of Indoor CO2 and PM2.5](https://mlsanity.com/paper/2603.06724/) (2026)
- [Generalizing Linear Autoencoder Recommenders with Decoupled Expected Quadratic Loss](https://mlsanity.com/paper/2603.07402/) (2026)
- [Unified Taxonomy for Multivariate Time Series Anomaly Detection using Deep Learning](https://mlsanity.com/paper/2603.18941/) (2026)
- [Progressive Learning with Anatomical Priors for Reliable Left Atrial Scar Segmentation from Late Gadolinium Enhancement MRI](https://mlsanity.com/paper/2603.26186/) (2026)
- [Ratio-based Loss Functions](https://mlsanity.com/paper/2605.05808/) (2026)

## Abstract (arXiv)

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.