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 7001–7025 of 10580 papers

TitleStatusHype
Learning Latent Word Representations for Domain Adaptation using Supervised Word Clustering—0
On the Provable Advantage of Unsupervised Pretraining—0
DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images—0
CenterGrasp: Object-Aware Implicit Representation Learning for Simultaneous Shape Reconstruction and 6-DoF Grasp Estimation—0
Learning Latent Topology for Graph Matching—0
Learning latent state representation for speeding up exploration—0
Learning latent representations for operational nitrogen response rate prediction—0
DNMDR: Dynamic Networks and Multi-view Drug Representations for Safe Medication Recommendation—0
Census-Independent Population Estimation using Representation Learning—0
Learning Language Representations with Logical Inductive Bias—0
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding—0
Learning Job Titles Similarity from Noisy Skill Labels—0
Learning Invariant Representation via Contrastive Feature Alignment for Clutter Robust SAR Target Recognition—0
On the Unintended Social Bias of Training Language Generation Models with News Articles—0
Cell Variational Information Bottleneck Network—0
Learning Invariant Representation and Risk Minimized for Unsupervised Accent Domain Adaptation—0
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations—0
Ontologically Grounded Multi-sense Representation Learning for Semantic Vector Space Models—0
Learning Interpretable Style Embeddings via Prompting LLMs—0
On unsupervised-supervised risk and one-class neural networks—0
CellSegmenter: unsupervised representation learning and instance segmentation of modular images—0
OOD Aware Supervised Contrastive Learning—0
AR-NeRF: Unsupervised Learning of Depth and Defocus Effects from Natural Images with Aperture Rendering Neural Radiance Fields—0
AES Systems Are Both Overstable And Oversensitive: Explaining Why And Proposing Defenses—0
About contrastive unsupervised representation learning for classification and its convergence—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