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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7201–7225 of 10307 papers

TitleStatusHype
TAS: Distilling Arbitrary Teacher and Student via a Hybrid Assistant—0
Buildings Classification using Very High Resolution Satellite Imagery—0
Task Adaptation of Reinforcement Learning-based NAS Agents through Transfer Learning—0
Task-Attentive Transformer Architecture for Continual Learning of Vision-and-Language Tasks Using Knowledge Distillation—0
Task-aware Adaptive Learning for Cross-domain Few-shot Learning—0
TASK AWARE MULTI-TASK LEARNING FOR SPEECH TO TEXT TASKS—0
Task-Aware Representation of Sentences for Generic Text Classification—0
Task-Driven Common Representation Learning via Bridge Neural Network—0
Bures Joint Distribution Alignment with Dynamic Margin for Unsupervised Domain Adaptation—0
Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation—0
TATL: Task Agnostic Transfer Learning for Skin Attributes Detection—0
Burgers' pinns with implicit euler transfer learning—0
Taxonomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer—0
Taxy.io@FinTOC-2020: Multilingual Document Structure Extraction using Transfer Learning—0
Tchebichef Transform Domain-based Deep Learning Architecture for Image Super-resolution—0
tCURLoRA: Tensor CUR Decomposition Based Low-Rank Parameter Adaptation and Its Application in Medical Image Segmentation—0
Teacher Guided Training: An Efficient Framework for Knowledge Transfer—0
Teacher-student curriculum learning for reinforcement learning—0
Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples—0
Teaching AI to Handle Exceptions: Supervised Fine-Tuning with Human-Aligned Judgment—0
Teaching pathology foundation models to accurately predict gene expression with parameter efficient knowledge transfer—0
Bypassing Optimization Complexity through Transfer Learning & Deep Neural Nets for Speech Intelligibility Improvement—0
Teaching with Uncertainty: Unleashing the Potential of Knowledge Distillation in Object Detection—0
Team Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competition—0
Team Innovators at SemEval-2022 for Task 8: Multi-Task Training with Hyperpartisan and Semantic Relation for Multi-Lingual News Article Similarity—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