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 5651–5700 of 10307 papers

TitleStatusHype
Social Learning: Towards Collaborative Learning with Large Language Models—0
SODA:Service Oriented Domain Adaptation Architecture for Microblog Categorization—0
Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis—0
Soft Representation Learning for Sparse Transfer—0
Software Vulnerability Prediction Knowledge Transferring Between Programming Languages—0
SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learning—0
SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe—0
Solvable Model for Inheriting the Regularization through Knowledge Distillation—0
Solving Euler equations with Multiple Discontinuities via Separation-Transfer Physics-Informed Neural Networks—0
Solving Large-scale Spatial Problems with Convolutional Neural Networks—0
Source Data Selection for Brain-Computer Interfaces based on Simple Features—0
Source data selection for out-of-domain generalization—0
Source-Free Cross-Modal Knowledge Transfer by Unleashing the Potential of Task-Irrelevant Data—0
Source-Free Domain Adaptation for Semantic Segmentation—0
Source-free Domain Adaptation Requires Penalized Diversity—0
Source-Free Unsupervised Domain Adaptation: A Survey—0
Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression—0
SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Editing—0
SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Color Editing—0
SpaceMeshLab: Spatial Context Memoization and Meshgrid Atrous Convolution Consensus for Semantic Segmentation—0
Space-Time Graph Neural Networks with Stochastic Graph Perturbations—0
SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet Variational Autoencoder for Hyperspectral Pixel Unmixing—0
SPARK: Self-supervised Personalized Real-time Monocular Face Capture—0
Sparse annotation strategies for segmentation of short axis cardiac MRI—0
Sparse Array Design for Direction Finding using Deep Learning—0
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