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We propose split-brain autoencoders, a straightforward modification of the traditional autoencoder architecture, for unsupervised representation learning.
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The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
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The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
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Imagenet classification with deep convolutional neural networks
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Indoor segmentation and support inference from rgbd images
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Generative adversarial nets
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Learning rich features from rgb-d images for object detection and segmentation
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Auto-encoding variational bayes
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Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification
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Learning visual groups from co-occurrences in space and time
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Data-dependent initializations of convolutional neural networks
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Learning representations for automatic colorization
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Shuffle and learn: unsupervised learning using temporal order verification
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Unsupervised learning of visual representations by solving jigsaw puzzles
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Imagenet large scale visual recognition challenge
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Ambient sound provides supervision for visual learning
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Context encoders: Feature learning by inpainting
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Colorful image colorization
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Adversarial feature learning
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Adversarially learned inference
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Colorization as a proxy task for visual understanding
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