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 6801–6850 of 10580 papers

TitleStatusHype
An unsupervised cluster-level based method for learning node representations of heterogeneous graphs in scientific papers—0
PADA: Pruning Assisted Domain Adaptation for Self-Supervised Speech RepresentationsCode0
ImpDet: Exploring Implicit Fields for 3D Object Detection—0
Weakly supervised causal representation learning—0
Controllable Augmentations for Video Representation Learning—0
Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning -- Extended VersionCode0
Investigating the Properties of Neural Network Representations in Reinforcement Learning—0
NL-FCOS: Improving FCOS through Non-Local Modules for Object Detection—0
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging DatasetsCode0
End-to-End Compressed Video Representation Learning for Generic Event Boundary Detection—0
Self-Supervised Image Representation Learning with Geometric Set Consistency—0
Robust Speaker Recognition with Transformers Using wav2vec 2.0—0
CD-Net: Histopathology Representation Learning using Pyramidal Context-Detail Network—0
S2-Net: Self-supervision Guided Feature Representation Learning for Cross-Modality Images—0
Isomorphic Cross-lingual Embeddings for Low-Resource Languages—0
Adversarial Representation Sharing: A Quantitative and Secure Collaborative Learning Framework—0
Single-Stream Multi-Level Alignment for Vision-Language PretrainingCode0
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos—0
Self-supervised Semantic Segmentation Grounded in Visual Concepts—0
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach—0
Leveraging unsupervised and weakly-supervised data to improve direct speech-to-speech translation—0
The Challenges of Continuous Self-Supervised Learning—0
Self-supervision through Random Segments with Autoregressive Coding (RandSAC)—0
Gated Domain-Invariant Feature Disentanglement for Domain Generalizable Object Detection—0
Feature Distribution Matching for Federated Domain GeneralizationCode0
Text Transformations in Contrastive Self-Supervised Learning: A Review—0
Towards Textual Out-of-Domain Detection without In-Domain Labels—0
Clustering units in neural networks: upstream vs downstream informationCode0
Representation Uncertainty in Self-Supervised Learning as Variational Inference—0
Rebalanced Siamese Contrastive Mining for Long-Tailed Recognition—0
Disentangling Patterns and Transformations from One Sequence of Images with Shape-invariant Lie Group Transformer—0
XTREME-S: Evaluating Cross-lingual Speech Representations—0
Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL—0
Attention Aided CSI Wireless Localization—0
Iwin: Human-Object Interaction Detection via Transformer with Irregular Windows—0
Deep reinforcement learning guided graph neural networks for brain network analysis—0
Graph-Text Multi-Modal Pre-training for Medical Representation LearningCode0
Symmetry-Based Representations for Artificial and Biological General Intelligence—0
Few-Shot Learning on Graphs—0
Explainability in Graph Neural Networks: An Experimental Survey—0
elBERto: Self-supervised Commonsense Learning for Question Answering—0
Graph Representation Learning with Individualization and Refinement—0
Multi-View Document Representation Learning for Open-Domain Dense Retrieval—0
X-Learner: Learning Cross Sources and Tasks for Universal Visual Representation—0
CapsNet for Medical Image Segmentation—0
Adversarial Learned Fair Representations using Dampening and Stacking—0
Generating Privacy-Preserving Process Data with Deep Generative Models—0
RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion—0
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding—0
Privacy-Preserving Speech Representation Learning using Vector Quantization—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