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 3726–3750 of 10307 papers

TitleStatusHype
Towards interpretable-by-design deep learning algorithms—0
Gendec: A Machine Learning-based Framework for Gender Detection from Japanese Names—0
Bit Cipher -- A Simple yet Powerful Word Representation System that Integrates Efficiently with Language Models—0
Towards Robust and Accurate Visual Prompting—0
Using Guided Transfer Learning to Predispose AI Agent to Learn Efficiently from Small RNA-sequencing Datasets—0
Physics-Enhanced Multi-fidelity Learning for Optical Surface Imprint—0
TransCDR: a deep learning model for enhancing the generalizability of cancer drug response prediction through transfer learning and multimodal data fusion for drug representationCode0
SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet Variational Autoencoder for Hyperspectral Pixel Unmixing—0
Harnessing Transformers: A Leap Forward in Lung Cancer Image Detection—0
Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces—0
Network Wide Evacuation Traffic Prediction in a Rapidly Intensifying Hurricane from Traffic Detectors and Facebook Movement Data: A Deep Learning Approach—0
Facilitating the sharing of electrophysiology data analysis results through in-depth provenance captureCode0
Investigating the Impact of Weight Sharing Decisions on Knowledge Transfer in Continual Learning—0
Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context LearningCode0
Language Semantic Graph Guided Data-Efficient LearningCode0
Mind's Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language ModelsCode0
Improving In-context Learning of Multilingual Generative Language Models with Cross-lingual AlignmentCode0
Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generation—0
Unlock the Power: Competitive Distillation for Multi-Modal Large Language Models—0
Fine-Tuning the Retrieval Mechanism for Tabular Deep Learning—0
FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition—0
TIAGo RL: Simulated Reinforcement Learning Environments with Tactile Data for Mobile Robots—0
Histopathologic Cancer DetectionCode0
VGSG: Vision-Guided Semantic-Group Network for Text-based Person Search—0
C-Procgen: Empowering Procgen with Controllable Contexts—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