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 9251–9300 of 10580 papers

TitleStatusHype
Robust Instruction-Following in a Situated Agent via Transfer-Learning from Text—0
Robust Natural Language Representation Learning for Natural Language Inference by Projecting Superficial Words out—0
GraphQA: Protein Model Quality Assessment using Graph Convolutional NetworkCode0
Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning—0
PatchFormer: A neural architecture for self-supervised representation learning on images—0
Lattice Representation Learning—0
Adversarial Privacy Preservation under Attribute Inference Attack—0
DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal SystemsCode0
Deep Multiple Instance Learning with Gaussian Weighting—0
Learning Good Policies By Learning Good Perceptual Models—0
Generalizing Reinforcement Learning to Unseen Actions—0
LARGE SCALE REPRESENTATION LEARNING FROM TRIPLET COMPARISONS—0
On unsupervised-supervised risk and one-class neural networks—0
Combining graph and sequence information to learn protein representations—0
On the Unintended Social Bias of Training Language Generation Models with News Articles—0
Discriminability Distillation in Group Representation Learning—0
COMPANYNAME11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery—0
Trajectory representation learning for Multi-Task NMRDPs planning—0
Unsupervised Hierarchical Graph Representation Learning with Variational Bayes—0
Wyner VAE: A Variational Autoencoder with Succinct Common Representation Learning—0
Stablizing Adversarial Invariance Induction by Discriminator Matching—0
Zero-Shot Policy Transfer with Disentangled Attention—0
Variational pSOM: Deep Probabilistic Clustering with Self-Organizing Maps—0
The Visual Task Adaptation Benchmark—0
Towards Interpretable Molecular Graph Representation Learning—0
Improving Generative Visual Dialog by Answering Diverse QuestionsCode0
CNN-based RGB-D Salient Object Detection: Learn, Select and Fuse—0
Large-scale representation learning from visually grounded untranscribed speech—0
Representation Learning for Electronic Health Records—0
HyperLearn: A Distributed Approach for Representation Learning in Datasets With Many Modalities—0
Dual Encoder-Decoder based Generative Adversarial Networks for Disentangled Facial Representation Learning—0
Research Commentary on Recommendations with Side Information: A Survey and Research Directions—0
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure DetectionCode0
Towards Shape Biased Unsupervised Representation Learning for Domain Generalization—0
Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis—0
Visuomotor Understanding for Representation Learning of Driving Scenes—0
Towards Unsupervised Segmentation of Extreme Weather Events—0
State Representation Learning from Demonstration—0
PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes—0
Representation Learning in Geology and GilBERT—0
IR2Vec: LLVM IR based Scalable Program EmbeddingsCode0
Semantic and Visual Similarities for Efficient Knowledge Transfer in CNN Training—0
Distributed representation of patients and its use for medical cost prediction—0
Neural Oblivious Decision Ensembles for Deep Learning on Tabular DataCode0
Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder—0
Query Obfuscation Semantic Decomposition—0
Learning First-Order Symbolic Representations for Planning from the Structure of the State Space—0
Scientific Discourse Tagging for Evidence ExtractionCode0
Video Representation Learning by Dense Predictive CodingCode0
Latent Multi-view Semi-Supervised ClassificationCode0
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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