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 7251–7300 of 10580 papers

TitleStatusHype
A Dynamic Network and Representation LearningApproach for Quantifying Economic Growth fromSatellite Imagery—0
Abnormality-Driven Representation Learning for Radiology Imaging—0
Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning—0
POC-SLT: Partial Object Completion with SDF Latent Transformers—0
Adaptive Structure-constrained Robust Latent Low-Rank Coding for Image Recovery—0
Distributed Decision Trees—0
CauSkelNet: Causal Representation Learning for Human Behaviour Analysis—0
Learning ECG Signal Features Without Backpropagation Using Linear Laws—0
A Simple Self-Supervised ECG Representation Learning Method via Manipulated Temporal-Spatial Reverse Detection—0
Distortion-Disentangled Contrastive Learning—0
DisTop: Discovering a Topological representation to learn diverse and rewarding skills—0
Point Clouds Are Specialized Images: A Knowledge Transfer Approach for 3D Understanding—0
CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation—0
Learning Dynamic Hierarchical Models for Anytime Scene Labeling—0
Learning Dynamic Graph Embeddings with Neural Controlled Differential Equations—0
Learning Dynamic Embeddings from Temporal Interactions—0
Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning—0
Distinctive Feature Codec: Adaptive Segmentation for Efficient Speech Representation—0
Learning-driven Zero Trust in Distributed Computing Continuum Systems—0
PointGAT: A quantum chemical property prediction model integrating graph attention and 3D geometry—0
Learning Downstream Task by Selectively Capturing Complementary Knowledge from Multiple Self-supervisedly Learning Pretexts—0
Learning Causal Domain-Invariant Temporal Dynamics for Few-Shot Action Recognition—0
Distilling Vision-Language Pretraining for Efficient Cross-Modal Retrieval—0
Are Synthetic Time-series Data Really not as Good as Real Data?—0
Learning Domain Invariant Representations for Generalizable Person Re-Identification—0
Learning Distributional Token Representations from Visual Features—0
Learning Distributed Representations for Multilingual Text Sequences—0
PointPatchRL -- Masked Reconstruction Improves Reinforcement Learning on Point Clouds—0
Distilling Localization for Self-Supervised Representation Learning—0
Pointspectrum: Equivariance Meets Laplacian Filtering for Graph Representation Learning—0
Learning dissection trajectories from expert surgical videos via imitation learning with equivariant diffusion—0
Learning Disentangled Speech Representations—0
Learning Disentangled Representation with Mutual Information Maximization for Real-Time UAV Tracking—0
A representation-learning game for classes of prediction tasks—0
Learning disentangled representations with the Wasserstein Autoencoder—0
Learning disentangled representations with the Wasserstein Autoencoder—0
Causal Representation Learning with Observational Grouping for CXR Classification—0
A polar prediction model for learning to represent visual transformations—0
Learning Disentangled Representations for Perceptual Point Cloud Quality Assessment via Mutual Information Minimization—0
Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization—0
Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification—0
Learning Disentangled Audio Representations through Controlled Synthesis—0
Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learning—0
Causal Representation Learning with Generative Artificial Intelligence: Application to Texts as Treatments—0
A Representation Learning Framework for Multi-Source Transfer Parsing—0
A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis—0
Contrastive Video Representation Learning via Adversarial Perturbations—0
Learning Discriminative Representation with Signed Laplacian Restricted Boltzmann Machine—0
Learning Discriminative Representations for Semantic Cross Media Retrieval—0
Learning Deep Similarity Models with Focus Ranking for Fabric Image Retrieval—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