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 6776–6800 of 10580 papers

TitleStatusHype
Learning Representations of Hierarchical Slates in Collaborative Filtering—0
Learning Representations of Emotional Speech with Deep Convolutional Generative Adversarial Networks—0
No Free Lunch in Self Supervised Representation Learning—0
Domain-Agnostic Clustering with Self-Distillation—0
Learning Representations of Affect from Speech—0
Learning Representations in Reinforcement Learning: an Information Bottleneck Approach—0
Learning Representations in Reinforcement Learning:An Information Bottleneck Approach—0
Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection—0
Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network—0
ARVideo: Autoregressive Pretraining for Self-Supervised Video Representation Learning—0
Learning Representations from Audio-Visual Spatial Alignment—0
Learning Representations for Pixel-based Control: What Matters and Why?—0
Domain-Adversarial and Conditional State Space Model for Imitation Learning—0
Learning Representations for Incomplete Time Series Clustering—0
Learning Representations for Detecting Abusive Language—0
Domain Adaptive Graph Classification—0
ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques—0
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation—0
DOMAIN ADAPTATION VIA DISTRIBUTION AND REPRESENTATION MATCHING: A CASE STUDY ON TRAINING DATA SELECTION VIA REINFORCEMENT LEARNING—0
DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning—0
Nonlinear spiked covariance matrices and signal propagation in deep neural networks—0
Learning Representations by Humans, for Humans—0
Learning Representations by Contrasting Clusters While Bootstrapping Instances—0
Domain Adaptation Meets Disentangled Representation Learning and Style Transfer—0
Learning Representation over Dynamic Graph using Aggregation-Diffusion Mechanism—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