← Learning Types
Deep Learning › Learning Types ›

Representation Learning

4516 directly classified papers

Papers per year

Papers

Learning Deep Parsimonious Representations NIPS 2016
Matching Networks for One Shot Learning NIPS 2016
Towards Conceptual Compression NIPS 2016
Domain Separation Networks NIPS 2016
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes NIPS 2016
Diffusion-Convolutional Neural Networks NIPS 2016
Residual Networks Behave Like Ensembles of Relatively Shallow Networks NIPS 2016
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections NIPS 2016
Maximal Sparsity with Deep Networks? NIPS 2016
TI-Pooling: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks CVPR 2016
Learning Deep Representation for Imbalanced Classification CVPR 2016
Learning Local Image Descriptors With Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions CVPR 2016
Efficient 3D Room Shape Recovery From a Single Panorama CVPR 2016
Structured Prediction of Unobserved Voxels From a Single Depth Image CVPR 2016
Recombinator Networks: Learning Coarse-To-Fine Feature Aggregation CVPR 2016
Sparse Coding for Classification via Discrimination Ensemble CVPR 2016
TenSR: Multi-Dimensional Tensor Sparse Representation CVPR 2016
Hierarchical Recurrent Neural Encoder for Video Representation With Application to Captioning CVPR 2016
SketchNet: Sketch Classification With Web Images CVPR 2016
Mining Discriminative Triplets of Patches for Fine-Grained Classification CVPR 2016
A Key Volume Mining Deep Framework for Action Recognition CVPR 2016
A Task-Oriented Approach for Cost-Sensitive Recognition CVPR 2016
Walk and Learn: Facial Attribute Representation Learning From Egocentric Video and Contextual Data CVPR 2016
Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification CVPR 2016
Automatic Content-Aware Color and Tone Stylization CVPR 2016