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 5401–5450 of 10580 papers

TitleStatusHype
Controllable Paraphrase Generation with a Syntactic Exemplar—0
Independence Promoted Graph Disentangled Networks—0
Independence Constrained Disentangled Representation Learning from Epistemological Perspective—0
A Review of Text Style Transfer using Deep Learning—0
Defining Words with Words: Beyond the Distributional Hypothesis—0
Incremental user embedding modeling for personalized text classification—0
Defining and Measuring Disentanglement for non-Independent Factors of Variation—0
C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation—0
Breaking Shallow Limits: Task-Driven Pixel Fusion for Gap-free RGBT Tracking—0
Learning Geospatial Region Embedding with Heterogeneous Graph—0
Anomaly Detection with Joint Representation Learning of Content and Connection—0
LMSOC: An approach for socially sensitive pretraining—0
Increasing the Efficiency of Policy Learning for Autonomous Vehicles by Multi-Task Representation Learning—0
Learning Good Policies By Learning Good Perceptual Models—0
Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing Industry—0
Learning Graph Search Heuristics—0
Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection—0
Revisiting the role of heterophily in graph representation learning: An edge classification perspective—0
Learning Hidden Markov Models with Distributed State Representations for Domain Adaptation—0
Learning Hierarchical Features with Joint Latent Space Energy-Based Prior—0
Learning Hierarchical Graph Representation for Image Manipulation Detection—0
Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection—0
Incorporating visual features into word embeddings: A bimodal autoencoder-based approach—0
Breaking Down Word Semantics from Pre-trained Language Models through Layer-wise Dimension Selection—0
Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding—0
Divide and Conquer Self-Supervised Learning for High-Content Imaging—0
BreakGPT: Leveraging Large Language Models for Predicting Asset Price Surges—0
Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations—0
Incorporating Global Information in Local Attention for Knowledge Representation Learning—0
Learning Image Representations by Completing Damaged Jigsaw Puzzles—0
Learning Improved Representations by Transferring Incomplete Evidence Across Heterogeneous Tasks—0
Learning in Factored Domains with Information-Constrained Visual Representations—0
Incorporating GAN for Negative Sampling in Knowledge Representation Learning—0
Incorporating Domain Knowledge into Health Recommender Systems using Hyperbolic Embeddings—0
Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning—0
Learning Internal Representations (COLT 1995)—0
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models—0
Learning Interpretable Fair Representations—0
Defeats GAN: A Simpler Model Outperforms in Knowledge Representation Learning—0
Adversarial Deep Learning in EEG Biometrics—0
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning—0
Local Distance Preserving Auto-encoders using Continuous k-Nearest Neighbours Graphs—0
Lorentzian Distance Learning—0
Learning Job Titles Similarity from Noisy Skill Labels—0
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding—0
Learning Language Representations with Logical Inductive Bias—0
Towards Understanding How Transformers Learn In-context Through a Representation Learning Lens—0
In-Context Learning for Few-Shot Nested Named Entity Recognition—0
Learning latent state representation for speeding up exploration—0
Deep Within-Class Covariance Analysis for Robust Deep Audio Representation Learning—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