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 7251–7300 of 10580 papers

TitleStatusHype
DeepFracture: A Generative Approach for Predicting Brittle Fractures with Neural Discrete Representation Learning—0
Audio-Visual Collaborative Representation Learning for Dynamic Saliency Prediction—0
pNNCLR: Stochastic Pseudo Neighborhoods for Contrastive Learning based Unsupervised Representation Learning Problems—0
POC-SLT: Partial Object Completion with SDF Latent Transformers—0
Deep Feature Learning for Wireless Spectrum Data—0
Holder Recommendations using Graph Representation Learning & Link Prediction—0
Deep Feature Learning for Graphs—0
An end-to-end Neural Network Framework for Text Clustering—0
HNCSE: Advancing Sentence Embeddings via Hybrid Contrastive Learning with Hard Negatives—0
HMSN: Hyperbolic Self-Supervised Learning by Clustering with Ideal Prototypes—0
Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction—0
Point Clouds Are Specialized Images: A Knowledge Transfer Approach for 3D Understanding—0
HLogformer: A Hierarchical Transformer for Representing Log Data—0
HJE: Joint Convolutional Representation Learning for Knowledge Hypergraph Completion—0
An End-to-End Model for Time Series Classification In the Presence of Missing Values—0
HiTRANS: A Hierarchical Transformer Network for Nested Named Entity Recognition—0
Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis—0
DeeperGCN: Training Deeper GCNs with Generalized Aggregation Functions—0
Bio2Token: All-atom tokenization of any biomolecular structure with Mamba—0
PointGAT: A quantum chemical property prediction model integrating graph attention and 3D geometry—0
HistoPerm: A Permutation-Based View Generation Approach for Improving Histopathologic Feature Representation Learning—0
Progressive growing of self-organized hierarchical representations for exploration—0
Deep End-to-end Unsupervised Anomaly Detection—0
ProductNet: a Collection of High-Quality Datasets for Product Representation Learning—0
HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features—0
PointMatch: A Consistency Training Framework for Weakly Supervised Semantic Segmentation of 3D Point Clouds—0
PointMoment:Mixed-Moment-based Self-Supervised Representation Learning for 3D Point Clouds—0
PointPatchRL -- Masked Reconstruction Improves Reinforcement Learning on Point Clouds—0
Deep Embedding Clustering Driven by Sample Stability—0
Product of Orthogonal Spheres Parameterization for Disentangled Representation Learning—0
HIMap: HybrId Representation Learning for End-to-end Vectorized HD Map Construction—0
An empirical study on speech restoration guided by self supervised speech representation—0
HiH: A Multi-modal Hierarchy in Hierarchy Network for Unconstrained Gait Recognition—0
PoissonSeg: Semi-Supervised Few-Shot Medical Image Segmentation via Poisson Learning—0
Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Graphs—0
Contrastive Rendering for Ultrasound Image Segmentation—0
Product Knowledge Graph Embedding for E-commerce—0
A polar prediction model for learning to represent visual transformations—0
PROFIT: A Specialized Optimizer for Deep Fine Tuning—0
HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction—0
Deep Domain Generalization via Conditional Invariant Adversarial Networks—0
Proceedings of The Third Workshop on Representation Learning for NLP—0
Autoencoders with Intrinsic Dimension Constraints for Learning Low Dimensional Image Representations—0
Spatial Object Recommendation with Hints: When Spatial Granularity Matters—0
Deep Dive into Semi-Supervised ELBO for Improving Classification Performance—0
Deep Discriminative Representation Learning with Attention Map for Scene Classification—0
FORESEE: Multimodal and Multi-view Representation Learning for Robust Prediction of Cancer Survival—0
Tag2Vec: Learning Tag Representations in Tag Networks—0
Polyline Generative Navigable Space Segmentation for Autonomous Visual Navigation—0
High Mutual Information in Representation Learning with Symmetric Variational Inference—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