conftrace_
← Learning Types
Deep Learning › Learning Types ›

Representation Learning

4,925 papers

Papers per year

7
51
606
487
'10
'15
'20
'25

Papers

Laughter Synthesis: Combining Seq2seq Modeling with Transfer Learning INTERSPEECH 2020
Speech-Image Semantic Alignment Does Not Depend on Any Prior Classification Tasks INTERSPEECH 2020
Length- and Noise-Aware Training Techniques for Short-Utterance Speaker Recognition INTERSPEECH 2020
Task-Oriented Dialog Generation with Enhanced Entity Representation INTERSPEECH 2020
Vector-Quantized Neural Networks for Acoustic Unit Discovery in the ZeroSpeech 2020 Challenge INTERSPEECH 2020
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation JMLR 2020
Silhouette Guided Point Cloud Reconstruction beyond Occlusion WACV 2020
Self-Attention Network for Skeleton-based Human Action Recognition WACV 2020
Long-Short Graph Memory Network for Skeleton-based Action Recognition WACV 2020
Model-Agnostic Metric for Zero-Shot Learning WACV 2020
Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization WACV 2020
Geometric Image Correspondence Verification by Dense Pixel Matching WACV 2020
Image Hashing via Linear Discriminant Learning WACV 2020
Audio-Visual Model Distillation Using Acoustic Images WACV 2020
Combinational Class Activation Maps for Weakly Supervised Object Localization WACV 2020
DTWNet: a Dynamic Time Warping Network NIPS 2019
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs NIPS 2019
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders NIPS 2019
CPM-Nets: Cross Partial Multi-View Networks NIPS 2019
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction NIPS 2019
Implicit Semantic Data Augmentation for Deep Networks NIPS 2019
Large Scale Adversarial Representation Learning NIPS 2019
Augmented Neural ODEs NIPS 2019
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks NIPS 2019
Recurrent Space-time Graph Neural Networks NIPS 2019