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 8001–8050 of 10580 papers

TitleStatusHype
GWPT: A Green Word-Embedding-based POS Tagger—0
Decomposing Bilexical Dependencies into Semantic and Syntactic Vectors—0
Revisiting Embeddings for Graph Neural Networks—0
Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations—0
Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network—0
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework—0
ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learning—0
Evidentiality-aware Retrieval for Overcoming Abstractiveness in Open-Domain Question Answering—0
Graph-based Unsupervised Disentangled Representation Learning via Multimodal Large Language Models—0
Robust Speaker Recognition with Transformers Using wav2vec 2.0—0
Robust Subspace Discovery by Block-diagonal Adaptive Locality-constrained Representation—0
GraphBit: Bitwise Interaction Mining via Deep Reinforcement Learning—0
Revisiting Image Reconstruction for Semi-supervised Semantic Segmentation—0
GUITAR: Gradient Pruning toward Fast Neural Ranking—0
Revisiting Metric Learning for Few-Shot Image Classification—0
Revisiting Self-supervised Learning of Speech Representation from a Mutual Information Perspective—0
Cross view link prediction by learning noise-resilient representation consensus—0
Revisiting Spurious Correlation in Domain Generalization—0
Decompose More and Aggregate Better: Two Closer Looks at Frequency Representation Learning for Human Motion Prediction—0
Guiding Representation Learning in Deep Generative Models with Policy Gradients—0
Decomposed Mutual Information Estimation for Contrastive Representation Learning—0
Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data—0
Guided Variational Autoencoder for Disentanglement Learning—0
Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search—0
A Unified Data Representation Learning for Non-parametric Two-sample Testing—0
Guided Generative Adversarial Neural Network for Representation Learning and High Fidelity Audio Generation using Fewer Labelled Audio Data—0
Decoder-free Robustness Disentanglement without (Additional) Supervision—0
Rewarding Episodic Visitation Discrepancy for Exploration in Reinforcement Learning—0
Robust Representation Learning with Self-Distillation for Domain Generalization—0
Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables—0
Guided contrastive self-supervised pre-training for automatic speech recognition—0
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples—0
Robust Representation Learning via Perceptual Similarity Metrics—0
Robust Representation Learning for Unified Online Top-K Recommendation—0
A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audio—0
Robust Representation Learning for Unreliable Partial Label Learning—0
Beyond H&E: Unlocking Pathological Insights with Polarization via Self-supervised Learning—0
Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach—0
Robust Representation Learning of Biomedical Names—0
Rich-Observation Reinforcement Learning with Continuous Latent Dynamics—0
Graph Contrastive Learning with Generative Adversarial Network—0
Robust Salient Object Detection on Compressed Images Using Convolutional Neural Networks—0
Distributed Representations of Entities in Open-World Knowledge Graphs—0
Riemannian Nearest-Regularized Subspace Classification for Polarimetric SAR images—0
Graph Contrastive Pre-training for Effective Theorem Reasoning—0
CSCNET: Class-Specified Cascaded Network for Compositional Zero-Shot Learning—0
GRU-TV: Time- and velocity-aware GRU for patient representation on multivariate clinical time-series data—0
Decentralized Complete Dictionary Learning via ^4-Norm Maximization—0
RL-Based Method for Benchmarking the Adversarial Resilience and Robustness of Deep Reinforcement Learning Policies—0
Pseudo-Euclidean Attract-Repel Embeddings for Undirected Graphs—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