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 7926–7950 of 10580 papers

TitleStatusHype
Representation Transfer for Differentially Private Drug Sensitivity Prediction—0
Representing Compositionality based on Multiple Timescales Gated Recurrent Neural Networks with Adaptive Temporal Hierarchy for Character-Level Language Models—0
Cross-Modal Retrieval and Synthesis (X-MRS): Closing the Modality Gap in Shared Representation Learning—0
Representing Spatial Trajectories as Distributions—0
Reproducible, incremental representation learning with Rosetta VAE—0
Reprogramming Foundational Large Language Models(LLMs) for Enterprise Adoption for Spatio-Temporal Forecasting Applications: Unveiling a New Era in Copilot-Guided Cross-Modal Time Series Representation Learning—0
Reprogramming Language Models for Molecular Representation Learning—0
Reprogramming Pretrained Language Models for Protein Sequence Representation Learning—0
Repurposing Foundation Model for Generalizable Medical Time Series Classification—0
HandMIM: Pose-Aware Self-Supervised Learning for 3D Hand Mesh Estimation—0
Handling Heterophily in Recommender Systems with Wavelet Hypergraph Diffusion—0
Research Commentary on Recommendations with Side Information: A Survey and Research Directions—0
Research on Domain Information Mining and Theme Evolution of Scientific Papers—0
Input-independent Attention Weights Are Expressive Enough: A Study of Attention in Self-supervised Audio Transformers—0
Research Team Identification Based on Representation Learning of Academic Heterogeneous Information Network—0
Graffin: Stand for Tails in Imbalanced Node Classification—0
Residual Connections Encourage Iterative Inference—0
Residual Connections Harm Generative Representation Learning—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Residual Contrastive Learning: Unsupervised Representation Learning from Residuals—0
AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers—0
Residual Relaxation for Multi-view Representation Learning—0
Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models—0
Cross-Patient Pseudo Bags Generation and Curriculum Contrastive Learning for Imbalanced Multiclassification of Whole Slide Image—0
Revisiting Metric Learning for Few-Shot Image Classification—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