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 9576–9600 of 10307 papers

TitleStatusHype
KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus—0
A Neural Network Model for Part-Of-Speech Tagging of Social Media Texts—0
Tel(s)-Telle(s)-Signs: Highly Accurate Automatic Crosslingual Hypernym Discovery—0
Simple Semantic Annotation and Situation Frames: Two Approaches to Basic Text Understanding in LORELEI—0
Sketch-a-Classifier: Sketch-based Photo Classifier Generation—0
Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial LossCode0
A Unified Framework for Domain Adaptation using Metric Learning on ManifoldsCode0
Decoupling Dynamics and Reward for Transfer LearningCode0
Deep learning approach to Fourier ptychographic microscopyCode0
Capsule networks for low-data transfer learning—0
Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification—0
Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification—0
Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition—0
Dropping Networks for Transfer Learning—0
Light-weight Head Pose Invariant Gaze Tracking—0
Deep cross-domain building extraction for selective depth estimation from oblique aerial imagery—0
A New Channel Boosted Convolutional Neural Network using Transfer Learning—0
Taskonomy: Disentangling Task Transfer LearningCode0
Towards Symbolic Reinforcement Learning with Common SenseCode0
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion RateCode0
Multi-task Learning for Universal Sentence Embeddings: A Thorough Evaluation using Transfer and Auxiliary Tasks—0
CactusNets: Layer Applicability as a Metric for Transfer Learning—0
Direct Network Transfer: Transfer Learning of Sentence Embeddings for Semantic Similarity—0
Putting Question-Answering Systems into Practice: Transfer Learning for Efficient Domain Customization—0
Robustness via Deep Low-Rank Representations—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