SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 7026–7050 of 10307 papers

TitleStatusHype
Learning Robust, Transferable Sentence Representations for Text Classification—0
Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolution—0
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning—0
Similarity of Pre-trained and Fine-tuned Representations—0
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization—0
Learning Shape Features and Abstractions in 3D Convolutional Neural Networks for Detecting Alzheimer's Disease—0
A Comparison of Few-Shot Learning Methods for Underwater Optical and Sonar Image Classification—0
A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings—0
An information-Theoretic Approach to Semi-supervised Transfer Learning—0
Simple and Effective Transfer Learning for Neuro-Symbolic Integration—0
Learning structures of the French clinical language:development and validation of word embedding models using 21 million clinical reports from electronic health records—0
Learning Student-Friendly Teacher Networks for Knowledge Distillation—0
Learning Task Automata for Reinforcement Learning using Hidden Markov Models—0
Learning Tensors in Reproducing Kernel Hilbert Spaces with Multilinear Spectral Penalties—0
Text-to-Code Generation with Modality-relative Pre-training—0
Learning the Localization Function: Machine Learning Approach to Fingerprinting Localization—0
Simple Control Baselines for Evaluating Transfer Learning—0
SimpleMTOD: A Simple Language Model for Multimodal Task-Oriented Dialogue with Symbolic Scene Representation—0
Learning to Adapt Credible Knowledge in Cross-lingual Sentiment Analysis—0
An Information-Theoretic Approach to Transferability in Task Transfer Learning—0
Learning to Ask Screening Questions for Job Postings—0
Learning To Avoid Negative Transfer in Few Shot Transfer Learning—0
Learning to be Safe: Deep RL with a Safety Critic—0
Learning to Branch for Multi-Task Learning—0
A Compare-Aggregate Model with Latent Clustering for Answer Selection—0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified