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 5801–5825 of 10307 papers

TitleStatusHype
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities—0
Model Inversion Attack against Transfer Learning: Inverting a Model without Accessing It—0
Leveraging universality of jet taggers through transfer learning—0
BabyNet: Reconstructing 3D faces of babies from uncalibrated photographs—0
GSDA: Generative Adversarial Network-based Semi-Supervised Data Augmentation for Ultrasound Image Classification—0
A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data—0
A survey of underwater acoustic data classification methods using deep learning for shoreline surveillance—0
Reprogramming FairGANs with Variational Auto-Encoders: A New Transfer Learning Model—0
Transferring Dual Stochastic Graph Convolutional Network for Facial Micro-expression Recognition—0
Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems—0
Unsupervised Alignment of Distributional Word Embeddings—0
Rethinking Task Sampling for Few-shot Vision-Language Transfer LearningCode0
How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting—0
Multi-Agent Policy Transfer via Task Relationship Modeling—0
Adaptive Trajectory Prediction via Transferable GNN—0
Towards Inadequately Pre-trained Models in Transfer Learning—0
Data augmentation with mixtures of max-entropy transformations for filling-level classification—0
HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks—0
Discriminability-Transferability Trade-Off: An Information-Theoretic PerspectiveCode0
HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal DataCode0
Learning from Few Examples: A Summary of Approaches to Few-Shot Learning—0
Generalization Through The Lens Of Leave-One-Out ErrorCode0
Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimization—0
Self-supervised learning for analysis of temporal and morphological drug effects in cancer cell imaging dataCode0
Exploration of Various Deep Learning Models for Increased Accuracy in Automatic Polyp Detection—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