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An Analysis of Single-Layer Networks in Unsupervised Feature Learning
TLDR
The results show that large numbers of hidden nodes and dense feature extraction are critical to achieving high performance—so critical, in fact, that when these parameters are pushed to their limits, they achieve state-of-the-art performance on both CIFAR-10 and NORB using only a single layer of features. Expand
Learning Structured Output Representation using Deep Conditional Generative Models
TLDR
A deep conditional generative model for structured output prediction using Gaussian latent variables is developed, trained efficiently in the framework of stochastic gradient variational Bayes, and allows for fast prediction using Stochastic feed-forward inference. Expand
Efficient sparse coding algorithms
TLDR
These algorithms are applied to natural images and it is demonstrated that the inferred sparse codes exhibit end-stopping and non-classical receptive field surround suppression and, therefore, may provide a partial explanation for these two phenomena in V1 neurons. Expand
Multimodal Deep Learning
TLDR
This work presents a series of tasks for multimodal learning and shows how to train deep networks that learn features to address these tasks, and demonstrates cross modality feature learning, where better features for one modality can be learned if multiple modalities are present at feature learning time. Expand
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
TLDR
The convolutional deep belief network is presented, a hierarchical generative model which scales to realistic image sizes and is translation-invariant and supports efficient bottom-up and top-down probabilistic inference. Expand
Generative Adversarial Text to Image Synthesis
TLDR
A novel deep architecture and GAN formulation is developed to effectively bridge advances in text and image modeling, translating visual concepts from characters to pixels. Expand
Evaluation of output embeddings for fine-grained image classification
TLDR
This project shows that compelling classification performance can be achieved on fine-grained categories even without labeled training data, and establishes a substantially improved state-of-the-art on the Animals with Attributes and Caltech-UCSD Birds datasets. Expand
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
TLDR
This paper proposes a simple yet effective method for detecting any abnormal samples, which is applicable to any pre-trained softmax neural classifier, and obtains the class conditional Gaussian distributions with respect to (low- and upper-level) features of the deep models under Gaussian discriminant analysis. Expand
Self-taught learning: transfer learning from unlabeled data
TLDR
An approach to self-taught learning that uses sparse coding to construct higher-level features using the unlabeled data to form a succinct input representation and significantly improve classification performance. Expand
Deep learning for detecting robotic grasps
TLDR
This work presents a two-step cascaded system with two deep networks, where the top detections from the first are re-evaluated by the second, and shows that this method improves performance on an RGBD robotic grasping dataset, and can be used to successfully execute grasps on two different robotic platforms. Expand
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