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 4076–4100 of 10307 papers

TitleStatusHype
Findings of the Covid-19 MLIA Machine Translation Task—0
Finding the Most Transferable Tasks for Brain Image Segmentation—0
Generalized User Representations for Transfer Learning—0
Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach—0
Fine-grained domain classification using Transformers—0
Confidence Preserving Machine for Facial Action Unit Detection—0
Deception Detection with Feature-Augmentation by soft Domain Transfer—0
Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach—0
Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery—0
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions—0
Fine-Grained Temporal Relation Extraction—0
Fine-grained Temporal Relation Extraction with Ordered-Neuron LSTM and Graph Convolutional Networks—0
Fine-Grained Vehicle Classification with Unsupervised Parts Co-occurrence Learning—0
Decentralized Federated Learning via Mutual Knowledge Transfer—0
Fine-to-coarse Knowledge Transfer For Low-Res Image Classification—0
FINETUNA: Fine-tuning Accelerated Molecular Simulations—0
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation—0
Adaptive Transfer Learning for Multi-Label Emotion Classification—0
Generalized Zero and Few-Shot Transfer for Facial Forgery Detection—0
Dec-Adapter: Exploring Efficient Decoder-Side Adapter for Bridging Screen Content and Natural Image Compression—0
Generalized Graphon Process: Convergence of Graph Frequencies in Stretched Cut Distance—0
Multilingual Approach to Joint Speech and Accent Recognition with DNN-HMM Framework—0
DEArt: Dataset of European Art—0
Adaptive Transfer Learning: a simple but effective transfer learning—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