Randomized decision trees and forests have a rich history in machine learning and have seen considerable success in application, perhaps particularly so for computer vision. However, they face a fundamental limitation: given enough data, the number of nodes in decision trees will grow exponentially with depth. For certain applications, for example on mobile or embedded processors, memory is a limited resource, and so the exponential growth of trees limits their depth, and thus their potential accuracy. This paper proposes decision jungles, revisiting the idea of ensembles of rooted decision directed acyclic graphs (DAGs), and shows these to be compact and powerful discriminative models for classification. Unlike conventional decision trees that only allow one path to every node, a DAG in a decision jungle allows multiple paths from the root to each leaf. We present and compare two new node merging algorithms that jointly optimize both the features and the structure of the DAGs efficiently. During training, node splitting and node merging are driven by the minimization of exactly the same objective function, here the weighted sum of entropies at the leaves. Results on varied datasets show that, compared to decision forests and several other baselines, decision jungles require dramatically less memory while considerably improving generalization.

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We also test the direct maximization of Kullback-Leibler (KL) divergence between feature distributions. As presented in Table A.1, the direct maximization of Direct maximize KL divergence between feature distributions. We further conduct ablation studies focusing on directly maximizing the Kullback-Leibler (KL) divergence between feature distributions of peer Bayesian neural networks (as in setting d in Table A.1). Table A.2, the results for both ResNet20 and ResNet32 BNN models demonstrate that using optimal "*" means Bayesian neural networks that are initialized with the mean value from the pre-trained The results are shown in Table A.3. Figure A.1: Comparison of optimal transport distance between the parameter distributions of peer A.1, it is clear that our proposed method, which promotes A.2, it is clear that our proposed method, which promotes diversity in the feature

In this section, we expand on our observation that similarity between training samples is not binary. Consider the images shown in Figure 6. As a consequence, any similarity between the anchor image and the so-called'negative' examples is completely ignored. Further, all'positive' examples are considered to be The batch size is set to 16000. We train on 4 A100 GPUs.