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 5676–5700 of 10307 papers

TitleStatusHype
Sparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning—0
Sparse coding for multitask and transfer learning—0
Sparse Contrastive Learning of Sentence Embeddings—0
Sparse Optimization for Transfer Learning: A L0-Regularized Framework for Multi-Source Domain Adaptation—0
Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of Actions—0
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs—0
Spatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images—0
Spatial Transfer Learning with Simple MLP—0
Spatial Transformer Network with Transfer Learning for Small-scale Fine-grained Skeleton-based Tai Chi Action Recognition—0
Spatio-Temporal Crop Aggregation for Video Representation Learning—0
Spatiotemporal Modeling for Crowd Counting in Videos—0
Spatio-Temporal Multi-Subgraph GCN for 3D Human Motion Prediction—0
SatSwinMAE: Efficient Autoencoding for Multiscale Time-series Satellite Imagery—0
Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning—0
Speaker Generation—0
Speaking style adaptation in Text-To-Speech synthesis using Sequence-to-sequence models with attention—0
SPECI: Skill Prompts based Hierarchical Continual Imitation Learning for Robot Manipulation—0
SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization—0
SpectFormer: Frequency and Attention is what you need in a Vision Transformer—0
Spectral-DP: Differentially Private Deep Learning through Spectral Perturbation and Filtering—0
Spectro-Temporal RF Identification using Deep Learning—0
Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios—0
Speech-Based Depression Prediction Using Encoder-Weight-Only Transfer Learning and a Large Corpus—0
Speech-Image Semantic Alignment Does Not Depend on Any Prior Classification Tasks—0
Speech Recognition Rescoring with Large Speech-Text Foundation Models—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