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 8651–8700 of 10580 papers

TitleStatusHype
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices—0
Functional Regularization for Representation Learning: A Unified Theoretical PerspectiveCode0
A Causal-based Framework for Multimodal Multivariate Time Series Validation Enhanced by Unsupervised Deep Learning as an Enabler for Industry 4.0—0
LoCo: Local Contrastive Representation Learning—0
Predicting What You Already Know Helps: Provable Self-Supervised Learning—0
MusiCoder: A Universal Music-Acoustic Encoder Based on Transformers—0
Learning Attentive and Hierarchical Representations for 3D Shape Recognition—0
Accelerating Deep Learning with Millions of Classes—0
A Visual Analytics Framework for Contrastive Network Analysis—0
Learning to See in the Dark with Events—0
Generative View-Correlation Adaptation for Semi-Supervised Multi-View Learning—0
Leveraging Acoustic Images for Effective Self-Supervised Audio Representation LearningCode0
Deep Learning-based Pupil Center Detection for Fast and Accurate Eye Tracking System—0
CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis—0
Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs—0
A Survey on Concept Factorization: From Shallow to Deep Representation Learning—0
node2coords: Graph Representation Learning with Wasserstein Barycenters—0
flexgrid2vec: Learning Efficient Visual Representations Vectors—0
Out-of-distribution Generalization via Partial Feature Decorrelation—0
Unsupervised Generative Adversarial Alignment Representation for Sheet music, Audio and Lyrics—0
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation—0
Low Dimensional State Representation Learning with Reward-shaped Priors—0
Privacy-preserving Voice Analysis via Disentangled Representations—0
Learning Video Representations from Textual Web Supervision—0
Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases—0
COMET: Convolutional Dimension Interaction for Collaborative Filtering—0
Representation Learning with Video Deep InfoMax—0
Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification—0
Contrastive Visual-Linguistic PretrainingCode0
Self-supervised Learning for Large-scale Item Recommendations—0
Real-World Multi-Domain Data Applications for Generalizations to Clinical Settings—0
METEOR: Learning Memory and Time Efficient Representations from Multi-modal Data Streams—0
Deep Learning based, end-to-end metaphor detection in Greek language with Recurrent and Convolutional Neural Networks—0
Discovering Traveling Companions using Autoencoders—0
Video Representation Learning by Recognizing Temporal Transformations—0
Unsupervised Heterogeneous Coupling Learning for Categorical Representation—0
Multi-label Contrastive Predictive Coding—0
Interpretable Foreground Object Search As Knowledge Distillation—0
Mixture Representation Learning with Coupled Autoencoders—0
Deep Representation Learning For Multimodal Brain Networks—0
A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning—0
Recent Advances in Network-based Methods for Disease Gene PredictionCode0
MIX'EM: Unsupervised Image Classification using a Mixture of Embeddings—0
Sparse Linear Networks with a Fixed Butterfly Structure: Theory and Practice—0
Self-Supervised Learning of Context-Aware Pitch Prosody Representations—0
GRADE: Graph Dynamic Embedding—0
Self-Supervised Representation Learning for Detection of ACL Tear Injury in Knee MR VideosCode0
Bitcoin Transaction Forecasting with Deep Network Representation Learning—0
Deep Representation Learning and Clustering of Traffic Scenarios—0
Two-Level Adversarial Visual-Semantic Coupling for Generalized Zero-shot Learning—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