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 9151–9200 of 10580 papers

TitleStatusHype
Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood MatchingCode0
Combining graph and sequence information to learn protein representations—0
Robust Instruction-Following in a Situated Agent via Transfer-Learning from Text—0
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget—0
Octave Graph Convolutional Network—0
Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning—0
Towards Interpretable Molecular Graph Representation Learning—0
Unsupervised Hierarchical Graph Representation Learning with Variational Bayes—0
Wyner VAE: A Variational Autoencoder with Succinct Common Representation Learning—0
Stablizing Adversarial Invariance Induction by Discriminator Matching—0
The Visual Task Adaptation Benchmark—0
Self-Supervised Policy Adaptation—0
Trajectory representation learning for Multi-Task NMRDPs planning—0
Variational pSOM: Deep Probabilistic Clustering with Self-Organizing Maps—0
Zero-Shot Policy Transfer with Disentangled Attention—0
PatchFormer: A neural architecture for self-supervised representation learning on images—0
Adversarial Privacy Preservation under Attribute Inference Attack—0
COMPANYNAME11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery—0
Learning Good Policies By Learning Good Perceptual Models—0
Deep End-to-end Unsupervised Anomaly Detection—0
ROBUST DISCRIMINATIVE REPRESENTATION LEARNING VIA GRADIENT RESCALING: AN EMPHASIS REGULARISATION PERSPECTIVE—0
An Information Theoretic Approach to Distributed Representation Learning—0
Reject Illegal Inputs: Scaling Generative Classifiers with Supervised Deep Infomax—0
ICNN: INPUT-CONDITIONED FEATURE REPRESENTATION LEARNING FOR TRANSFORMATION-INVARIANT NEURAL NETWORK—0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention—0
GraphQA: Protein Model Quality Assessment using Graph Convolutional NetworkCode0
A bi-diffusion based layer-wise sampling method for deep learning in large graphs—0
Lattice Representation Learning—0
Deep Multiple Instance Learning with Gaussian Weighting—0
Generalizing Reinforcement Learning to Unseen Actions—0
Robust Natural Language Representation Learning for Natural Language Inference by Projecting Superficial Words out—0
DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal SystemsCode0
UNITER: UNiversal Image-TExt Representation LearningCode1
Improving Generative Visual Dialog by Answering Diverse QuestionsCode0
CNN-based RGB-D Salient Object Detection: Learn, Select and Fuse—0
Research Commentary on Recommendations with Side Information: A Survey and Research Directions—0
Dual Encoder-Decoder based Generative Adversarial Networks for Disentangled Facial Representation Learning—0
Representation Learning for Electronic Health Records—0
HyperLearn: A Distributed Approach for Representation Learning in Datasets With Many Modalities—0
Large-scale representation learning from visually grounded untranscribed speech—0
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure DetectionCode0
Towards Shape Biased Unsupervised Representation Learning for Domain Generalization—0
Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis—0
Revealing the Importance of Semantic Retrieval for Machine Reading at ScaleCode1
Multimodal Multitask Representation Learning for Pathology Biobank Metadata PredictionCode1
Towards Unsupervised Segmentation of Extreme Weather Events—0
Visuomotor Understanding for Representation Learning of Driving Scenes—0
State Representation Learning from Demonstration—0
PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes—0
Representation Learning in Geology and GilBERT—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