# DeepTracker: Visualizing the Training Process of Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1808.08531 · HTML: https://mlsanity.com/paper/1808.08531/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2018.

## Built on by (most cited first, 7 of 7)

- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [T3-Vis: a visual analytic framework for Training and fine-Tuning Transformers in NLP](https://mlsanity.com/paper/2108.13587/) (2021)
- [Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles](https://mlsanity.com/paper/2103.10229/) (2021)
- [Interactive Analysis of CNN Robustness](https://mlsanity.com/paper/2110.07667/) (2021)
- [Guidelines for the Regularization of Gammas in Batch Normalization for Deep Residual Networks](https://mlsanity.com/paper/2205.07260/) (2022)
- [Towards data-driven filters in Paraview](https://mlsanity.com/paper/2108.05196/) (2021)
- [A Comparative Visual Analytics Framework for Evaluating Evolutionary Processes in Multi-objective Optimization](https://mlsanity.com/paper/2308.05640/) (2023)

## Abstract (arXiv)

Deep convolutional neural networks (CNNs) have achieved remarkable success in various fields. However, training an excellent CNN is practically a trial-and-error process that consumes a tremendous amount of time and computer resources. To accelerate the training process and reduce the number of trials, experts need to understand what has occurred in the training process and why the resulting CNN behaves as such. However, current popular training platforms, such as TensorFlow, only provide very little and general information, such as training/validation errors, which is far from enough to serve this purpose. To bridge this gap and help domain experts with their training tasks in a practical environment, we propose a visual analytics system, DeepTracker, to facilitate the exploration of the rich dynamics of CNN training processes and to identify the unusual patterns that are hidden behind the huge amount of training log. Specifically,we combine a hierarchical index mechanism and a set of hierarchical small multiples to help experts explore the entire training log from different levels of detail. We also introduce a novel cube-style visualization to reveal the complex correlations among multiple types of heterogeneous training data including neuron weights, validation images, and training iterations. Three case studies are conducted to demonstrate how DeepTracker provides its users with valuable knowledge in an industry-level CNN training process, namely in our case, training ResNet-50 on the ImageNet dataset. We show that our method can be easily applied to other state-of-the-art "very deep" CNN models.