← Learning Types
Deep Learning › Learning Types ›

Representation Learning

4516 directly classified papers

Papers per year

Papers

Towards Conceptual Compression NIPS 2016
Exponential Family Embeddings NIPS 2016
Tagger: Deep Unsupervised Perceptual Grouping NIPS 2016
TI-Pooling: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks CVPR 2016
Fashion Style in 128 Floats: Joint Ranking and Classification Using Weak Data for Feature Extraction CVPR 2016
Deep Contrast Learning for Salient Object Detection CVPR 2016
Deep Saliency With Encoded Low Level Distance Map and High Level Features CVPR 2016
DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection CVPR 2016
What Players Do With the Ball: A Physically Constrained Interaction Modeling CVPR 2016
Highlight Detection With Pairwise Deep Ranking for First-Person Video Summarization 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
Comparative Deep Learning of Hybrid Representations for Image Recommendations CVPR 2016
Rain Streak Removal Using Layer Priors CVPR 2016
Visualizing and Understanding Deep Texture Representations CVPR 2016
Online Collaborative Learning for Open-Vocabulary Visual Classifiers CVPR 2016
RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection CVPR 2016
Learning Deep Features for Discriminative Localization CVPR 2016
Seeing Through the Human Reporting Bias: Visual Classifiers From Noisy Human-Centric Labels CVPR 2016