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 7176–7200 of 10307 papers

TitleStatusHype
Efficient Learning of Less Biased Models with Transfer Learning—0
Counterfactual Thinking for Long-tailed Information Extraction—0
MoCo-Pretraining Improves Representations and Transferability of Chest X-ray Models—0
Cross-lingual Transfer Learning for Pre-trained Contextualized Language Models—0
Re-examining Routing Networks for Multi-task Learning—0
Knowledge Mining and Transferring for Domain Adaptive Object DetectionCode0
CCT-Net: Category-Invariant Cross-Domain Transfer for Medical Single-to-Multiple Disease Diagnosis—0
Compute- and Memory-Efficient Reinforcement Learning with Latent Experience Replay—0
Efficient Graph Neural Architecture Search—0
Constraining Latent Space to Improve Deep Self-Supervised e-Commerce Products Embeddings for Downstream Tasks—0
Contextual Transformation Networks for Online Continual Learning—0
Self-Supervised Transfer Learning for Hand Mesh Recovery From Binocular Images—0
CDS: Cross-Domain Self-Supervised Pre-Training—0
Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach—0
Pre-training Text-to-Text Transformers to Write and Reason with Concepts—0
A Deep Learning Approach for Diabetic Retinopathy detection using Transfer Learning—0
Probabilistic Meta-Learning for Bayesian Optimization—0
CIZSL++: Creativity Inspired Generative Zero-Shot LearningCode0
Generalization in data-driven models of primary visual cortex—0
Network-Agnostic Knowledge Transfer from Latent Dataset for Medical Image Segmentation—0
Unsupervised Task Clustering for Multi-Task Reinforcement LearningCode0
Transferable Unsupervised Robust Representation Learning—0
XLA: A Robust Unsupervised Data Augmentation Framework for Cross-Lingual NLP—0
Zero-shot Transfer Learning for Gray-box Hyper-parameter Optimization—0
Unified Principles For Multi-Source Transfer Learning Under Label Shifts—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