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 8701–8750 of 10580 papers

TitleStatusHype
Contrastive Representation Learning for Acoustic Parameter Estimation—0
Physiological Signal Embeddings (PHASE) via Interpretable Stacked Models—0
Contrastive Representation Learning for 3D Protein Structures—0
Contrastive Representation Learning for Predicting Solar Flares from Extremely Imbalanced Multivariate Time Series Data—0
Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities—0
Self-supervision meets kernel graph neural models: From architecture to augmentations—0
Contrastive Representation Learning for Hand Shape Estimation—0
Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence—0
Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections—0
Contrastive Representation Learning Helps Cross-institutional Knowledge Transfer: A Study in Pediatric Ventilation Management—0
Contrastive Representation Learning with Trainable Augmentation Channel—0
Contrastive Self-Supervised Learning As Neural Manifold Packing—0
Contrastive Semantic Similarity Learning for Image Captioning Evaluation with Intrinsic Auto-encoder—0
Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation—0
Contrastive Semi-supervised Learning for ASR—0
Contrastive Separative Coding for Self-supervised Representation Learning—0
PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations—0
Contrastive String Representation Learning using Synthetic Data—0
PIDRo: Parallel Isomeric Attention with Dynamic Routing for Text-Video Retrieval—0
Piece-wise Matching Layer in Representation Learning for ECG Classification—0
Contrastive Unlearning: A Contrastive Approach to Machine Unlearning—0
Contrastive Unsupervised Learning for Speech Emotion Recognition—0
Contrastive Unsupervised Learning of World Model with Invariant Causal Features—0
Contrastive Video-Language Learning with Fine-grained Frame Sampling—0
Contrastive Word Embedding Learning for Neural Machine Translation—0
Contrast Phase Classification with a Generative Adversarial Network—0
Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition—0
Piecewise-Velocity Model for Learning Continuous-time Dynamic Node Representations—0
Contributions to Representation Learning with Graph Autoencoders and Applications to Music Recommendation—0
Control-Aware Representations for Model-based Reinforcement Learning—0
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning—0
Control False Negative Instances In Contrastive Learning To ImproveLong-tailed Item Categorization—0
Controllable Augmentations for Video Representation Learning—0
Controllable Chest X-Ray Report Generation from Longitudinal Representations—0
Controllable Invariance through Adversarial Feature Learning—0
ControlVAE: Controllable Variational Autoencoder—0
Controlled Text Generation Using Dictionary Prior in Variational Autoencoders—0
Controlling Computation versus Quality for Neural Sequence Models—0
Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems—0
Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement—0
Controversy Detection: a Text and Graph Neural Network Based Approach—0
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware—0
Convexified Message-Passing Graph Neural Networks—0
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space—0
ConViTac: Aligning Visual-Tactile Fusion with Contrastive Representations—0
Adaptive Online Incremental Learning for Evolving Data Streams—0
Convolutional Dictionary Pair Learning Network for Image Representation Learning—0
Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding—0
CooPre: Cooperative Pretraining for V2X Cooperative Perception—0
Coordinated Transformer with Position \& Sample-aware Central Loss for Anatomical Landmark Detection—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
6CiteBERTAvg.58.8—Unverified
7BioBERTAvg.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