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Efficient Learning of CNNs using Patch Based Features
Alon Brutzkus · Amir Globerson · Eran Malach · Alon Regev Netser · Shai Shalev-Shwartz

Wed Jul 20 02:30 PM -- 02:35 PM (PDT) @ Ballroom 3 & 4
in Theory »

Recent work has demonstrated the effectiveness of using patch based representations when learning from image data. Here we provide theoretical support for this observation, by showing that a simple semi-supervised algorithm that uses patch statistics can efficiently learn labels produced by a one-hidden-layer Convolutional Neural Network (CNN). Since CNNs are known to be computationally hard to learn in the worst case, our analysis holds under some distributional assumptions. We show that these assumptions are necessary and sufficient for our results to hold. We verify that the distributional assumptions hold on real-world data by experimenting on the CIFAR-10 dataset, and find that the analyzed algorithm outperforms a vanilla one-hidden-layer CNN. Finally, we demonstrate that by running the algorithm in a layer-by-layer fashion we can build a deep model which gives further improvements, hinting that this method provides insights about the behavior of deep CNNs.

Keywords: DL: Algorithms · DL: Other Representation Learning · DL: Theory · T: Deep Learning · T: Learning Theory

Author Information

Alon Brutzkus (Tel Aviv University)
Amir Globerson (Tel Aviv University, Google)
Eran Malach (Hebrew University Jerusalem Israel)
Alon Regev Netser (The Hebrew University of Jerusalem)
Shai Shalev-Shwartz

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