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 8051–8100 of 10307 papers

TitleStatusHype
Inferring the source of official texts: can SVM beat ULMFiT?Code0
Causal Transfer for Imitation Learning and Decision Making under Sensor-shift—0
DEEVA: A Deep Learning and IoT Based Computer Vision System to Address Safety and Security of Production Sites in Energy Industry—0
Modelling the Neuroanatomical Progression of Alzheimer's Disease and Posterior Cortical Atrophy—0
The Utility of Feature Reuse: Transfer Learning in Data-Starved RegimesCode0
Two-Level Attention-based Fusion Learning for RGB-D Face RecognitionCode0
AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning—0
Transferring Dense Pose to Proximal Animal ClassesCode1
Entangled Watermarks as a Defense against Model ExtractionCode1
LEEP: A New Measure to Evaluate Transferability of Learned Representations—0
Meta-Transfer Learning for Zero-Shot Super-ResolutionCode1
A Free-Energy Principle for Representation Learning—0
Adapted tree boosting for Transfer Learning—0
On Leveraging Pretrained GANs for Generation with Limited DataCode1
Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance NormalizationCode0
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic EnvironmentsCode0
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICACode1
Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Cameras—0
Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization—0
Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning—0
Towards Learning a Universal Non-Semantic Representation of SpeechCode0
Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANsCode1
How Transferable are the Representations Learned by Deep Q Agents?—0
Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks—0
Knowledge Transfer between Buildings for Seismic Damage Diagnosis through Adversarial Learning—0
Estimating Q(s,s') with Deep Deterministic Dynamics GradientsCode1
Detection and Classification of Astronomical Targets with Deep Neural Networks in Wide Field Small Aperture Telescopes—0
A Comparative Study of Western and Chinese Classical Music based on Soundscape Models—0
oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions—0
The continuous categorical: a novel simplex-valued exponential familyCode1
CNN-based approach for glaucoma diagnosis using transfer learning and LBP-based data augmentation—0
Rethinking the Hyperparameters for Fine-tuningCode1
Compressing BERT: Studying the Effects of Weight Pruning on Transfer LearningCode1
A Systematic Comparison of Architectures for Document-Level Sentiment ClassificationCode0
Efficient Deep Reinforcement Learning via Adaptive Policy TransferCode0
Distance-Based Regularisation of Deep Networks for Fine-TuningCode1
SentenceMIM: A Latent Variable Language ModelCode1
Interpretable Multi-Headed Attention for Abstractive Summarization at Controllable Lengths—0
Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN—0
Multilinear Compressive Learning with Prior KnowledgeCode0
SpotTheFake: An Initial Report on a New CNN-Enhanced Platform for Counterfeit Goods Detection—0
Patient-Specific Finetuning of Deep Learning Models for Adaptive Radiotherapy in Prostate CT—0
CRL: Class Representative Learning for Image Classification—0
The Utility of General Domain Transfer Learning for Medical Language Tasks—0
Automatic lesion segmentation and Pathological Myopia classification in fundus images—0
A Sequence Matching Network for Polyphonic Sound Event Localization and Detection—0
A deep learning framework for solution and discovery in solid mechanicsCode1
A Set of Distinct Facial Traits Learned by Machines Is Not Predictive of Appearance Bias in the WildCode0
Leveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System—0
On the Value of Target Data in 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