Learning Explicit Deep Representations from Deep Kernel Networks
This work addresses scalability issues in deep kernel learning for researchers and practitioners handling large datasets, though it is incremental as it builds on existing DKN methods.
The paper tackles the computational inefficiency of Deep Kernel Networks (DKNs) on large-scale datasets by proposing Deep Map Networks (DMNs), which approximate DKNs with inner products for faster evaluation. Experiments on ImageCLEF and COREL5k show DMNs achieve similar accuracy to DKNs while being at least an order of magnitude faster.
Deep kernel learning aims at designing nonlinear combinations of multiple standard elementary kernels by training deep networks. This scheme has proven to be effective, but intractable when handling large-scale datasets especially when the depth of the trained networks increases; indeed, the complexity of evaluating these networks scales quadratically w.r.t. the size of training data and linearly w.r.t. the depth of the trained networks. In this paper, we address the issue of efficient computation in Deep Kernel Networks (DKNs) by designing effective maps in the underlying Reproducing Kernel Hilbert Spaces. Given a pretrained DKN, our method builds its associated Deep Map Network (DMN) whose inner product approximates the original network while being far more efficient. The design principle of our method is greedy and achieved layer-wise, by finding maps that approximate DKNs at different (input, intermediate and output) layers. This design also considers an extra fine-tuning step based on unsupervised learning, that further enhances the generalization ability of the trained DMNs. When plugged into SVMs, these DMNs turn out to be as accurate as the underlying DKNs while being at least an order of magnitude faster on large-scale datasets, as shown through extensive experiments on the challenging ImageCLEF and COREL5k benchmarks.