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 9276–9300 of 10307 papers

TitleStatusHype
Efficient Language Model Training through Cross-Lingual and Progressive Transfer LearningCode0
Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-TuningCode0
Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labelsCode0
Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer LearningCode0
Efficient Transfer Learning for Video-language Foundation ModelsCode0
eGAN: Unsupervised approach to class imbalance using transfer learningCode0
EiX-GNN : Concept-level eigencentrality explainer for graph neural networksCode0
EkoHate: Abusive Language and Hate Speech Detection for Code-switched Political Discussions on Nigerian TwitterCode0
Elastic Coupled Co-clustering for Single-Cell Genomic DataCode0
El Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC TasksCode0
Eliminating artefacts in Polarimetric Images using Deep LearningCode0
Ellipsis Resolution as Question Answering: An EvaluationCode0
ELTEX: A Framework for Domain-Driven Synthetic Data GenerationCode0
Embeddia at SemEval-2019 Task 6: Detecting Hate with Neural Network and Transfer Learning ApproachesCode0
Embedding neurophysiological signalsCode0
Embedding Ordinality to Binary Loss Function for Improving Solar Flare ForecastingCode0
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment DesignCode0
Emoji-Based Transfer Learning for Sentiment TasksCode0
Emotional Speech Recognition with Pre-trained Deep Visual ModelsCode0
Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to RCode0
Empowering Dual-Level Graph Self-Supervised Pretraining with Motif DiscoveryCode0
Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum LearningCode0
Empower Sequence Labeling with Task-Aware Neural Language ModelCode0
Emulating Brain-like Rapid Learning in Neuromorphic Edge ComputingCode0
Encodings for Prediction-based Neural Architecture SearchCode0
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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