SOTAVerified

Representation Learning

Representation Learning is a process in machine learning where algorithms extract meaningful patterns from raw data to create representations that are easier to understand and process. These representations can be designed for interpretability, reveal hidden features, or be used for transfer learning. They are valuable across many fundamental machine learning tasks like image classification and retrieval.

Deep neural networks can be considered representation learning models that typically encode information which is projected into a different subspace. These representations are then usually passed on to a linear classifier to, for instance, train a classifier.

Representation learning can be divided into:

  • Supervised representation learning: learning representations on task A using annotated data and used to solve task B
  • Unsupervised representation learning: learning representations on a task in an unsupervised way (label-free data). These are then used to address downstream tasks and reducing the need for annotated data when learning news tasks. Powerful models like GPT and BERT leverage unsupervised representation learning to tackle language tasks.

More recently, self-supervised learning (SSL) is one of the main drivers behind unsupervised representation learning in fields like computer vision and NLP.

Here are some additional readings to go deeper on the task:

( Image credit: Visualizing and Understanding Convolutional Networks )

Papers

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Showing 9701–9750 of 10580 papers

TitleStatusHype
Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention—0
Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders—0
Variational Autoencoders Pursue PCA Directions (by Accident)—0
Coupled Representation Learning for Domains, Intents and Slots in Spoken Language Understanding—0
Omni-directional Feature Learning for Person Re-identification—0
Recent Advances in Autoencoder-Based Representation Learning—0
An efficient supervised dictionary learning method for audio signal recognition—0
Transfer Learning using Representation Learning in Massive Open Online Courses—0
Facial Landmark Machines: A Backbone-Branches Architecture with Progressive Representation Learning—0
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification—0
Measuring and Characterizing Generalization in Deep Reinforcement Learning—0
Learning Dynamic Embeddings from Temporal Interactions—0
dynnode2vec: Scalable Dynamic Network Embedding—0
Learning Implicit Fields for Generative Shape ModelingCode0
Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding ManifoldCode0
Rare Event Detection using Disentangled Representation Learning—0
Deep Hierarchical Machine: a Flexible Divide-and-Conquer Architecture—0
Improving Clinical Predictions through Unsupervised Time Series Representation Learning—0
Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems—0
A Dynamic Network and Representation LearningApproach for Quantifying Economic Growth fromSatellite Imagery—0
Representation Learning for Treatment Effect Estimation from Observational DataCode0
A Comparative Study of Embedding Models in Predicting the Compositionality of Multiword Expressions—0
Representation Learning of Compositional DataCode0
Neural Interaction Transparency (NIT): Disentangling Learned Interactions for Improved Interpretability—0
Trading robust representations for sample complexity through self-supervised visual experience—0
The Graph-Based Behavior-Aware Recommendation for Interactive NewsCode0
Graph Node-Feature Convolution for Representation LearningCode0
Self-Supervised GANs via Auxiliary Rotation LossCode0
Learning State Representations in Complex Systems with Multimodal Data—0
Flexible Attributed Network EmbeddingCode0
Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision—0
A Differential Topological View of Challenges in Learning with Feedforward Neural Networks—0
Time-Aware and View-Aware Video Rendering for Unsupervised Representation Learning—0
Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles—0
Adversarial Classifier for Imbalanced Problems—0
Learning from Multiview Correlations in Open-Domain Videos—0
Unsupervised Multimodal Representation Learning across Medical Images and Reports—0
Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation LearningCode0
Representation Learning of Pedestrian Trajectories Using Actor-Critic Sequence-to-Sequence Autoencoder—0
Adversarial Autoencoders for Compact Representations of 3D Point CloudsCode0
Re-Identification with Consistent Attentive Siamese Networks—0
Learning Actionable Representations with Goal-Conditioned PoliciesCode0
Discriminative Graph Autoencoder—0
Grasp2Vec: Learning Object Representations from Self-Supervised GraspingCode0
Neural Predictive Belief Representations—0
SGR: Self-Supervised Spectral Graph Representation Learning—0
Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection—0
Adversarial Unsupervised Representation Learning for Activity Time-Series—0
Embedding Electronic Health Records for Clinical Information Retrieval—0
Co-Representation Learning For Classification and Novel Class Detection via Deep Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SciNCLAvg.81.8—Unverified
2SPECTERAvg.80—Unverified
3CiteomaticAvg.76—Unverified
4Sci-DeCLUTRAvg.66.6—Unverified
5SciBERTAvg.59.6—Unverified
6BioBERTAvg.58.8—Unverified
7CiteBERTAvg.58.8—Unverified
#ModelMetricClaimedVerifiedStatus
1top_model_weights_with_3d_21:1 Accuracy0.75—Unverified
#ModelMetricClaimedVerifiedStatus
1Resnet 18Accuracy (%)97.05—Unverified
#ModelMetricClaimedVerifiedStatus
1Morphological NetworkAccuracy97.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Max Margin ContrastiveSilhouette Score0.56—Unverified