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 5451–5500 of 10580 papers

TitleStatusHype
Attentive Spatio-Temporal Representation Learning for Diving Classification—0
Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning—0
Considerations for a PAP Smear Image Analysis System with CNN Features—0
Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations: a COVID-19 case-study—0
Consensus Graph Representation Learning for Better Grounded Image Captioning—0
Attentive Representation Learning with Adversarial Training for Short Text Clustering—0
Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution—0
Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning—0
Exploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networks—0
Exploiting Common Characters in Chinese and Japanese to Learn Cross-Lingual Word Embeddings via Matrix Factorization—0
Consensus Clustering With Unsupervised Representation Learning—0
ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similarity—0
AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing—0
Accelerating Representation Learning with View-Consistent Dynamics in Data-Efficient Reinforcement Learning—0
Maximizing Asynchronicity in Event-based Neural Networks—0
Bailing-TTS: Chinese Dialectal Speech Synthesis Towards Human-like Spontaneous Representation—0
Explaining Translationese: why are Neural Classifiers Better and what do they Learn?—0
Explaining Knowledge Graph Embedding via Latent Rule Learning—0
Explaining, Evaluating and Enhancing Neural Networks' Learned Representations—0
Connectional-Style-Guided Contextual Representation Learning for Brain Disease Diagnosis—0
Attentive Multi-View Deep Subspace Clustering Net—0
Explainable Trajectory Representation through Dictionary Learning—0
Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs—0
Explainable Recommender Systems via Resolving Learning Representations—0
Connecting Supervised and Unsupervised Sentence Embeddings—0
Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification—0
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging—0
Connecting Multi-modal Contrastive Representations—0
Explainability in Graph Neural Networks: An Experimental Survey—0
ExpertNet: A Symbiosis of Classification and Clustering—0
Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning—0
Expert Knowledge-guided Geometric Representation Learning for Magnetic Resonance Imaging-based Glioma Grading—0
Connecting Data to Mechanisms with Meta Structual Causal Model—0
Experience Grounds Language—0
ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis—0
Attention-wise masked graph contrastive learning for predicting molecular property—0
AKBR: Learning Adaptive Kernel-based Representations for Graph Classification—0
Expected path length on random manifolds—0
Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis—0
Attention to Detail: Fine-Scale Feature Preservation-Oriented Geometric Pre-training for AI-Driven Surrogate Modeling—0
Expand BERT Representation with Visual Information via Grounded Language Learning with Multimodal Partial Alignment—0
ExLM: Rethinking the Impact of [MASK] Tokens in Masked Language Models—0
Confounder Balancing in Adversarial Domain Adaptation for Pre-Trained Large Models Fine-Tuning—0
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning—0
Attention is All You Need? Good Embeddings with Statistics are enough:Large Scale Audio Understanding without Transformers/ Convolutions/ BERTs/ Mixers/ Attention/ RNNs or ....—0
A Joint Representation Learning and Feature Modeling Approach for One-class Recognition—0
Exemplar Learning for Medical Image Segmentation—0
Conformer-Based Self-Supervised Learning for Non-Speech Audio Tasks—0
Configurable Spatial-Temporal Hierarchical Analysis for Flexible Video Anomaly Detection—0
Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures—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