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 6426–6450 of 10307 papers

TitleStatusHype
Removing Rain Streaks via Task Transfer Learning—0
Renewing Iterative Self-labeling Domain Adaptation with Application to the Spine Motion Prediction—0
REPAINT: Knowledge Transfer in Deep Actor-Critic Reinforcement Learning—0
REPAINT: Knowledge Transfer in Deep Reinforcement Learning—0
Replay to Remember: Continual Layer-Specific Fine-tuning for German Speech Recognition—0
Rep-Net: Efficient On-Device Learning via Feature Reprogramming—0
RePreM: Representation Pre-training with Masked Model for Reinforcement Learning—0
Representational Distance Learning for Deep Neural Networks—0
Representational Transfer Learning for Matrix Completion—0
Benchmarking Deep Learning Frameworks for Automated Diagnosis of Ocular Toxoplasmosis: A Comprehensive Approach to Classification and Segmentation—0
Representation learning from videos in-the-wild: An object-centric approach—0
Adam Mickiewicz University’s English-Hausa Submissions to the WMT 2021 News Translation Task—0
Representation Purification for End-to-End Speech Translation—0
Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery—0
Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning—0
Representation Topology Divergence: A Method for Comparing Neural Network Representations.—0
Representation Transfer by Optimal Transport—0
Representation Transfer Learning via Multiple Pre-trained models for Linear Regression—0
Re-presenting a Story by Emotional Factors using Sentimental Analysis Method—0
Benchmarking Image Embeddings for E-Commerce: Evaluating Off-the Shelf Foundation Models, Fine-Tuning Strategies and Practical Trade-offs—0
Reprogramming FairGANs with Variational Auto-Encoders: A New Transfer Learning Model—0
Reprogramming Language Models for Molecular Representation Learning—0
Benchmarking of Lightweight Deep Learning Architectures for Skin Cancer Classification using ISIC 2017 Dataset—0
Repurposing 2D Diffusion Models with Gaussian Atlas for 3D Generation—0
Repurposing Decoder-Transformer Language Models for Abstractive Summarization—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