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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9451–9500 of 10580 papers

TitleStatusHype
Unsupervised Representation Learning of DNA Sequences—0
Extracting Visual Knowledge from the Internet: Making Sense of Image Data—0
Evolving Losses for Unlabeled Video Representation Learning—0
Data-to-text Generation with Entity ModelingCode0
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement DatasetCode0
DeepMDP: Learning Continuous Latent Space Models for Representation Learning—0
Quaternion Collaborative Filtering for Recommendation—0
Cross-Modal Interaction Networks for Query-Based Moment Retrieval in VideosCode0
Knowledge-Aware Deep Dual Networks for Text-Based Mortality Prediction—0
Flexibly Fair Representation Learning by Disentanglement—0
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph ClassificationCode0
Efficient Codebook and Factorization for Second Order Representation Learning—0
KERMIT: Generative Insertion-Based Modeling for Sequences—0
Pykg2vec: A Python Library for Knowledge Graph Embedding—0
RL-Based Method for Benchmarking the Adversarial Resilience and Robustness of Deep Reinforcement Learning Policies—0
Controllable Paraphrase Generation with a Syntactic Exemplar—0
Learning Representations by Maximizing Mutual Information Across ViewsCode0
Pre-training of Graph Augmented Transformers for Medication RecommendationCode0
Pretraining Methods for Dialog Context Representation Learning—0
Composition of Sentence Embeddings: Lessons from Statistical Relational Learning—0
Self-Supervised Representation Learning From Videos for Facial Action Unit DetectionCode0
Self-Supervised Representation Learning by Rotation Feature DecouplingCode0
AE2-Nets: Autoencoder in Autoencoder Networks—0
Document-Level N-ary Relation Extraction with Multiscale Representation Learning—0
Self-Discriminative Learning for Unsupervised Document Embedding—0
Bayesian Learning of Latent Representations of Language Structures—0
Entropy Minimization In Emergent Languages—0
Semantics-Aligned Representation Learning for Person Re-identificationCode0
Deep Adversarial Social RecommendationCode0
Unsupervised Model Selection for Variational Disentangled Representation Learning—0
Learning Representations by Humans, for Humans—0
Instance-Aware Representation Learning and Association for Online Multi-Person Tracking—0
Putting An End to End-to-End: Gradient-Isolated Learning of RepresentationsCode0
Discrete Infomax Codes for Supervised Representation Learning—0
Texture CNN for Thermoelectric Metal Pipe Image Classification—0
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling—0
Learning with Succinct Common Representation Based on Wyner's Common Information—0
Representation Learning for Dynamic Graphs: A Survey—0
Practical and Consistent Estimation of f-Divergences—0
MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network—0
Learning latent state representation for speeding up exploration—0
Incidence Networks for Geometric Deep Learning—0
Deep Representation Learning for Road Detection through Siamese Network—0
Graph Attention Auto-EncodersCode0
Robust Unsupervised Flexible Auto-weighted Local-Coordinate Concept Factorization for Image Clustering—0
Locality-Promoting Representation Learning—0
Learning Cross-Domain Representation with Multi-Graph Neural Network—0
Self-supervised audio representation learning for mobile devices—0
Hangul Fonts Dataset: a Hierarchical and Compositional Dataset for Investigating Learned Representations—0
The Journey is the Reward: Unsupervised Learning of Influential Trajectories—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