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 6301–6350 of 10580 papers

TitleStatusHype
Leveraging Semantic Representations Combined with Contextual Word Representations for Recognizing Textual Entailment in Vietnamese—0
Dynamic Traceback Learning for Medical Report Generation—0
Towards Achieving Perfect Multimodal Alignment—0
Motion Keyframe Interpolation for Any Human Skeleton via Temporally Consistent Point Cloud Sampling and Reconstruction—0
Motion Sensitive Contrastive Learning for Self-supervised Video Representation—0
Leveraging Orbital Information and Atomic Feature in Deep Learning Model—0
Classifying Diagrams and Their Parts using Graph Neural Networks: A Comparison of Crowd-Sourced and Expert Annotations—0
A Free-Energy Principle for Representation Learning—0
MPT-PAR:Mix-Parameters Transformer for Panoramic Activity Recognition—0
Active Multimodal Distillation for Few-shot Action Recognition—0
Text Descriptions are Compressive and Invariant Representations for Visual Learning—0
Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning—0
Leveraging Latent Representations of Speech for Indian Language Identification—0
Leveraging large language models for efficient representation learning for entity resolution—0
Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection—0
Leveraging Herpangina Data to Enhance Hospital-level Prediction of Hand-Foot-and-Mouth Disease Admissions Using UPTST—0
DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness—0
Classification of developmental and brain disorders via graph convolutional aggregation—0
A Simple Imitation Learning Method via Contrastive Regularization—0
Leveraging Fine-Grained Information and Noise Decoupling for Remote Sensing Change Detection—0
Leveraging Color Channel Independence for Improved Unsupervised Object Detection—0
Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI—0
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations—0
Dual Graph Representation Learning—0
Leveraging Auto-Distillation and Generative Self-Supervised Learning in Residual Graph Transformers for Enhanced Recommender Systems—0
MusicTM-Dataset for Joint Representation Learning among Sheet Music, Lyrics, and Musical Audio—0
Leveraging affinity cycle consistency to isolate factors of variation in learned representations—0
LetsMap: Unsupervised Representation Learning for Semantic BEV Mapping—0
Dual Graph Complementary Network—0
A Simple General Method for Detecting Textual Adversarial Examples—0
Dual-Granularity Contrastive Learning for Session-based Recommendation—0
Less Data, More Knowledge: Building Next Generation Semantic Communication Networks—0
Classes Are Not Equal: An Empirical Study on Image Recognition Fairness—0
Multi-Cast Attention Networks for Retrieval-based Question Answering and Response Prediction—0
Less can be more in contrastive learning—0
Dual Encoder-Decoder based Generative Adversarial Networks for Disentangled Facial Representation Learning—0
LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations—0
ClassContrast: Bridging the Spatial and Contextual Gaps for Node Representations—0
A Simple Framework for Uncertainty in Contrastive Learning—0
Length- and Noise-aware Training Techniques for Short-utterance Speaker Recognition—0
Multi-Dialectal Representation Learning of Sinitic Phonology—0
LegoNet: A Fast and Exact Unlearning Architecture—0
Learning Word Representations from Relational Graphs—0
Class-aware and Augmentation-free Contrastive Learning from Label Proportion—0
Multi-Domain Causal Representation Learning via Weak Distributional Invariances—0
Multi-Domain Self-Supervised Learning—0
A Simple Framework for Open-Vocabulary Zero-Shot Segmentation—0
MultiEarth 2022 -- Multimodal Learning for Earth and Environment Workshop and Challenge—0
TraceGrad: a Framework Learning Expressive SO(3)-equivariant Non-linear Representations for Electronic-Structure Hamiltonian Prediction—0
Active metric learning and classification using similarity queries—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