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An Analysis of Single-Layer Networks in Unsupervised Feature Learning
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.
Learning Structured Output Representation using Deep Conditional Generative Models
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.
Efficient sparse coding algorithms
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.
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
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.
Multimodal Deep Learning
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.
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
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.
Generative Adversarial Text to Image Synthesis
- Scott E. Reed, Zeynep Akata, Xinchen Yan, L. Logeswaran, B. Schiele, Honglak Lee
- Computer ScienceICML
- 17 May 2016
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.
Evaluation of output embeddings for fine-grained image classification
- Zeynep Akata, Scott E. Reed, D. Walter, Honglak Lee, B. Schiele
- Computer ScienceIEEE Conference on Computer Vision and Pattern…
- 30 September 2014
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.
Similarity of Neural Network Representations Revisited
A similarity index is introduced that measures the relationship between representational similarity matrices and does not suffer from this limitation of CCA.
Deep learning for detecting robotic grasps
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.