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 8326–8350 of 10307 papers

TitleStatusHype
300 GHz Radar Object Recognition based on Deep Neural Networks and Transfer Learning—0
ClusterFit: Improving Generalization of Visual RepresentationsCode0
How Does an Approximate Model Help in Reinforcement Learning?—0
Kernel learning for visual perceptionCode0
Self-Supervised Learning of Video-Induced Visual Invariances—0
Transfer Learning from an Auxiliary Discriminative Task for Unsupervised Anomaly Detection—0
An Automated Deep Learning Approach for Bacterial Image Classification—0
AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue—0
Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translation—0
Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia—0
Unsupervised Inflection Generation Using Neural Language Modeling—0
TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLPCode0
Applying Knowledge Transfer for Water Body Segmentation in Peru—0
Implicit Priors for Knowledge Sharing in Bayesian Neural Networks—0
Deep Neural Network Fingerprinting by Conferrable Adversarial ExamplesCode0
Is Discriminator a Good Feature Extractor?—0
Online Knowledge Distillation with Diverse PeersCode0
Cross-Domain Recommendation via Preference Propagation GraphNet—0
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer LearningCode0
Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative TransferCode0
Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain ActivityCode0
Comparing Unsupervised Word Translation Methods Step by Step—0
Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks—0
Better Transfer Learning with Inferred Successor Maps—0
Syntactically Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion Extraction—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