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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9101–9150 of 10580 papers

TitleStatusHype
Deep Unsupervised Active Learning on Learnable Graphs—0
Deep unsupervised anomaly detection—0
Deep Unsupervised Common Representation Learning for LiDAR and Camera Data using Double Siamese Networks—0
Deep Variational Luenberger-type Observer for Stochastic Video Prediction—0
Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses—0
Deep video representation learning: a survey—0
Deep Visual-Semantic Quantization for Efficient Image Retrieval—0
Self-Supervised Disentangled Representation Learning for Third-Person Imitation Learning—0
Deep Within-Class Covariance Analysis for Robust Audio Representation Learning—0
Deep Within-Class Covariance Analysis for Robust Deep Audio Representation Learning—0
Position-based Hash Embeddings For Scaling Graph Neural Networks—0
Defeats GAN: A Simpler Model Outperforms in Knowledge Representation Learning—0
Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection—0
Defining and Measuring Disentanglement for non-Independent Factors of Variation—0
Defining Words with Words: Beyond the Distributional Hypothesis—0
Position: Topological Deep Learning is the New Frontier for Relational Learning—0
Deformable Graph Transformer—0
DEFTri: A Few-Shot Label Fused Contextual Representation Learning For Product Defect Triage in e-Commerce—0
Degeneration in VAE: in the Light of Fisher Information Loss—0
Adaptive Contextual Embedding for Robust Far-View Borehole Detection—0
Déjà Vu Memorization in Vision-Language Models—0
Position Paper on Materials Design -- A Modern Approach—0
Delayed Bottlenecking: Alleviating Forgetting in Pre-trained Graph Neural Networks—0
Delineation of line patterns in images using B-COSFIRE filters—0
Possible principles for aligned structure learning agents—0
Dementia Severity Classification under Small Sample Size and Weak Supervision in Thick Slice MRI—0
DeMIAN: Deep Modality Invariant Adversarial Network—0
Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields—0
Demo2Vec: Learning Region Embedding with Demographic Information—0
Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases—0
Demystifying overcomplete nonlinear auto-encoders: fast SGD convergence towards sparse representation from random initialization—0
PPG-based singing voice conversion with adversarial representation learning—0
PPKE: Knowledge Representation Learning by Path-based Pre-training—0
Denoising Autoregressive Representation Learning—0
Denoising Cosine Similarity: A Theory-Driven Approach for Efficient Representation Learning—0
Tackling the Local Bias in Federated Graph Learning—0
Practical and Consistent Estimation of f-Divergences—0
Denoising with a Joint-Embedding Predictive Architecture—0
DenoMAE2.0: Improving Denoising Masked Autoencoders by Classifying Local Patches—0
Self-Supervised Disentangled Representation Learning for Robust Target Speech Extraction—0
Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics—0
Dense Contrastive Visual-Linguistic Pretraining—0
Morphological Network: How Far Can We Go with Morphological Neurons?—0
Dense Semantic Contrast for Self-Supervised Visual Representation Learning—0
Density-Based Bonuses on Learned Representations for Reward-Free Exploration in Deep Reinforcement Learning—0
Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research—0
Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification—0
Enhancing Word-Level Semantic Representation via Dependency Structure for Expressive Text-to-Speech Synthesis—0
DEPHN: Different Expression Parallel Heterogeneous Network using virtual gradient optimization for Multi-task Learning—0
DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representations—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