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 9651–9675 of 10307 papers

TitleStatusHype
Deep learning for affective computing: text-based emotion recognition in decision support—0
Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model—0
Transferable Pedestrian Motion Prediction Models at Intersections—0
Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled DataCode0
Coregionalised Locomotion Envelopes - A Qualitative Approach—0
General-Purpose Deep Point Cloud Feature ExtractorCode0
Transfer Learning with Neural AutoML—0
Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification—0
LSTD: A Low-Shot Transfer Detector for Object DetectionCode0
Knowledge Transfer with Jacobian Matching—0
Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the WildCode0
Classification of breast cancer histology images using transfer learning—0
Attention-Aware Generative Adversarial Networks (ATA-GANs)—0
Discriminative Label Consistent Domain Adaptation—0
Continual Lifelong Learning with Neural Networks: A Review—0
Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative DiseasesCode0
Sim-to-Real Optimization of Complex Real World Mobile Network with Imperfect Information via Deep Reinforcement Learning from Self-play—0
Learning Adversarially Fair and Transferable RepresentationsCode0
HWNet v2: An Efficient Word Image Representation for Handwritten Documents—0
Instance-based Inductive Deep Transfer Learning by Cross-Dataset Querying with Locality Sensitive Hashing—0
Putting a bug in ML: The moth olfactory network learns to read MNISTCode0
3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network—0
Universal Neural Machine Translation for Extremely Low Resource Languages—0
Paraphrasing Complex Network: Network Compression via Factor TransferCode0
Learning Robust and Adaptive Real-World Continuous Control Using Simulation and 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