Explores the ideas presented in Deep Ensembles: A Loss Landscape Perspective by Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan.
In the paper, the authors investigate the question - why deep ensembles work better than single deep neural networks?
In their investigation the authors figure out:
- Different snapshots (i.e. model from epoch 1, model from epoch 2, and so on) of a same model exhibit functional similarity. Hence, their ensemble is less likely to explore the different modes of local minimas in the optimization space.
- Different solutions of a same model (i.e. trained with different random initializations each time) exhibit functional dissimilarity. Hence, their ensemble is more likely to explore the different modes of local minimas in the optimization space.
Along with these fascinating findings, they present a number of different things that are useful to understand the dynamics of deep neural networks in general. To know more about them check out our report - Understanding the Effectivity of Ensembles in Deep Learning.
*_CIFAR10.ipynb: Shows the training process with three different architectures (SmallCNN, MediumCNN, and ResNet20v1) as per the paper (with minor modifications).*_Aug_Val_Acc_Ensembles.ipynb: Investigates how accuracy can be represented as a function of ensemble size.Visualizing_Function_Space_Similarity_*.ipynb: Investigates cosine similarity between weights collected from different snapshots and trajectories, prediction disagreement between different snapshots and trajectories, and presents tSNE visualizations of a how particular solution travels along the optimization landscape (not available for ResNet20v1).
Available here.
Thanks to Yannic Kilcher for his amazing explanation video of the paper which helped us pursue our experiments.
Thanks to Balaji Lakshminarayanan for providing feedback on the initial draft of the report and also for rectifying our mistake on the tSNE projections.