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 8201–8250 of 10580 papers

TitleStatusHype
Representation learning for maximization of MI, nonlinear ICA and nonlinear subspaces with robust density ratio estimation—0
Partial Domain Adaptation Using Selective Representation Learning For Class-Weight Computation—0
Deep unsupervised anomaly detection—0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph—0
Temporal Contrastive Graph Learning for Video Action Recognition and Retrieval—0
Multi-Model Least Squares-Based Recomputation Framework for Large Data Analysis—0
Depth as Attention for Face Representation LearningCode0
Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network—0
RRL: A Scalable Classifier for Interpretable Rule-Based Representation Learning—0
Robust Multi-view Representation Learning—0
Towards Powerful Graph Neural Networks: Diversity Matters—0
R-MONet: Region-Based Unsupervised Scene Decomposition and Representation via Consistency of Object Representations—0
Toward Understanding Supervised Representation Learning with RKHS and GAN—0
Cross-State Self-Constraint for Feature Generalization in Deep Reinforcement Learning—0
Rethinking 360deg Image Visual Attention Modelling With Unsupervised Learning.Code0
Ballroom Dance Movement Recognition Using a Smart Watch and Representation Learning—0
Towards Robust and Efficient Contrastive Textual Representation Learning—0
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations—0
Recursive Neighborhood Pooling for Graph Representation Learning—0
Recurrent Exploration Networks for Recommender Systems—0
Real-Time AutoML—0
Vi2CLR: Video and Image for Visual Contrastive Learning of Representation—0
Probabilistic Multimodal Representation Learning—0
Universal Sentence Representations Learning with Conditional Masked Language Model—0
PARTS: Unsupervised Segmentation With Slots, Attention and Independence Maximization—0
Parameterization of Hypercomplex Multiplications—0
Feature Interactive Representation for Point Cloud Registration—0
Contrastive Attention Maps for Self-Supervised Co-Localization—0
On the Importance of Looking at the Manifold—0
On the Importance of Distraction-Robust Representations for Robot Learning—0
Sequence Metric Learning as Synchronization of Recurrent Neural Networks—0
FASG: Feature Aggregation Self-training GCN for Semi-supervised Node Classification—0
Online Limited Memory Neural-Linear Bandits—0
Online Adversarial Purification based on Self-supervised Learning—0
Continual Lifelong Causal Effect Inference with Real World Evidence—0
Contextual Knowledge Distillation for Transformer Compression—0
Non-Markovian Predictive Coding For Planning In Latent Space—0
Non-maximum Suppression Also Closes the Variational Approximation Gap of Multi-object Variational Autoencoders—0
Exploring representation learning for flexible few-shot tasks—0
Exploring Geometry-Aware Contrast and Clustering Harmonization for Self-Supervised 3D Object Detection—0
Exploring Balanced Feature Spaces for Representation Learning—0
Switchable K-Class Hyperplanes for Noise-Robust Representation Learning—0
Consistent Instance Classification for Unsupervised Representation Learning—0
Neural Bayes: A Generic Parameterization Method for Unsupervised Learning—0
Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs—0
UserBERT: Self-supervised User Representation Learning—0
Attack-Guided Perceptual Data Generation for Real-World Re-Identification—0
A Text GAN for Language Generation with Non-Autoregressive Generator—0
Monolingual Word Sense Alignment as a Classification Problem—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