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 2501–2525 of 10307 papers

TitleStatusHype
Open Set Dandelion Network for IoT Intrusion Detection—0
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
Adapters: A Unified Library for Parameter-Efficient and Modular Transfer LearningCode4
Using Guided Transfer Learning to Predispose AI Agent to Learn Efficiently from Small RNA-sequencing Datasets—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
Physics-Enhanced Multi-fidelity Learning for Optical Surface Imprint—0
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
Network Wide Evacuation Traffic Prediction in a Rapidly Intensifying Hurricane from Traffic Detectors and Facebook Movement Data: A Deep Learning Approach—0
Investigating the Impact of Weight Sharing Decisions on Knowledge Transfer in Continual Learning—0
Facilitating the sharing of electrophysiology data analysis results through in-depth provenance captureCode0
Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces—0
Language Semantic Graph Guided Data-Efficient LearningCode0
Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context LearningCode0
Mind's Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language ModelsCode0
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
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
Improving In-context Learning of Multilingual Generative Language Models with Cross-lingual AlignmentCode0
FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition—0
PICS in Pics: Physics Informed Contour Selection for Rapid Image Segmentation—0
Developing a Named Entity Recognition Dataset for TagalogCode1
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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