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 4401–4450 of 10580 papers

TitleStatusHype
Domain Generalization via Selective Consistency Regularization for Time Series Classification—0
Characterizing the adversarial vulnerability of speech self-supervised learning—0
Domain Generalization -- A Causal Perspective—0
A Scalable and Effective Alternative to Graph Transformers—0
Invariant Causal Representation Learning—0
Domain-aware Self-supervised Pre-training for Label-Efficient Meme Analysis—0
Domain-aware Self-supervised Pre-training for Weakly-supervised Meme Analysis—0
Channel Mapping Based on Interleaved Learning with Complex-Domain MLP-Mixer—0
Domain Aligned CLIP for Few-shot Classification—0
Domain-Agnostic Prior for Transfer Semantic Segmentation—0
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning—0
A theory of representation learning gives a deep generalisation of kernel methods—0
Invariant Causal Representation Learning for Out-of-Distribution Generalization—0
InvariantOODG: Learning Invariant Features of Point Clouds for Out-of-Distribution Generalization—0
Domain-Agnostic Clustering with Self-Distillation—0
ARVideo: Autoregressive Pretraining for Self-Supervised Video Representation Learning—0
Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network—0
ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques—0
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective—0
Domain-Adversarial and Conditional State Space Model for Imitation Learning—0
A Second-Order Majorant Algorithm for Nonnegative Matrix Factorization—0
Domain Adaptive Graph Classification—0
Invariance & Causal Representation Learning: Prospects and Limitations—0
DOMAIN ADAPTATION VIA DISTRIBUTION AND REPRESENTATION MATCHING: A CASE STUDY ON TRAINING DATA SELECTION VIA REINFORCEMENT LEARNING—0
DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning—0
Domain Adaptation Meets Disentangled Representation Learning and Style Transfer—0
Challenging Assumptions in Learning Generic Text Style Embeddings—0
Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning—0
Intrinsic Geometric Information Transfer Learning on Multiple Graph-Structured Datasets—0
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images—0
Artificial Intelligence Augmented Medical Imaging Reconstruction in Radiation Therapy—0
Introducing Diminutive Causal Structure into Graph Representation Learning—0
Invariant and consistent: Unsupervised representation learning for few-shot visual recognition—0
Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging—0
Investigating and Explaining Feature and Representation Learning in Translationese Classification—0
ChainNet: Learning on Blockchain Graphs with Topological Features—0
Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions—0
Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning—0
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions—0
Causal Flow-based Variational Auto-Encoder for Disentangled Causal Representation Learning—0
Does Visual Self-Supervision Improve Learning of Speech Representations for Emotion Recognition?—0
Learning Generalizable Visual Representations via Interactive Gameplay—0
INTRA: Interaction Relationship-aware Weakly Supervised Affordance Grounding—0
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations—0
Does It Happen? Multi-hop Path Structures for Event Factuality Prediction with Graph Transformer Networks—0
cf2vec: Collaborative Filtering algorithm selection using graph distributed representations—0
CETransformer: Casual Effect Estimation via Transformer Based Representation Learning—0
Action-Sufficient State Representation Learning for Control with Structural Constraints—0
Does Corpus Quality Really Matter for Low-Resource Languages?—0
Do DALL-E and Flamingo Understand Each Other?—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