Papers › Exploring the Evolution of Hidden Activations with Live-Update Visualization
Exploring the Evolution of Hidden Activations with Live-Update Visualization
Xianglin Yang, Jin Song Dong
Monitoring the training of neural networks is essential for identifying potential data anomalies, enabling timely interventions and conserving significant computational resources. Apart from the commonly used metrics such as losses and validation accuracies, the hidden representation could give more insight into the model progression. To this end, we introduce SentryCam, an automated, real-time visualization tool that reveals the progression of hidden representations during training. Our results show that this visualization offers a more comprehensive view of the learning dynamics compared to basic metrics such as loss and accuracy over various datasets. Furthermore, we show that SentryCam could facilitate detailed analysis such as task transfer and catastrophic forgetting to a continual learning setting. The code is available at https://github.com/xianglinyang/SentryCam.
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