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 6801–6850 of 10307 papers

TitleStatusHype
Large Scale Transfer Learning for Differentially Private Image Classification—0
Large-scale Transfer Learning for Low-resource Spoken Language Understanding—0
LA-SACo: A Study of Learning Approaches for Sentiments Analysis inCode-Mixing Texts—0
LastResort at SemEval-2022 Task 5: Towards Misogyny Identification using Visual Linguistic Model Ensembles And Task-Specific Pretraining—0
Latent Alignment with Deep Set EEG Decoders—0
Latent Function Decomposition for Forecasting Li-ion Battery Cells Capacity: A Multi-Output Convolved Gaussian Process Approach—0
Latent Hinge-Minimax Risk Minimization for Inference from a Small Number of Training Samples—0
Latent-Insensitive autoencoders for Anomaly Detection—0
Latent Intuitive Physics: Learning to Transfer Hidden Physics from A 3D Video—0
Latent Object Characteristics Recognition with Visual to Haptic-Audio Cross-modal Transfer Learning—0
Latent User Linking for Collaborative Cross Domain Recommendation—0
Lautum Regularization for Semi-supervised Transfer Learning—0
LaViP:Language-Grounded Visual Prompts—0
LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models—0
Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned Large Language Models—0
Noisy Data Meets Privacy: Training Local Models with Post-Processed Remote Queries—0
Leaf Identification Using a Deep Convolutional Neural Network—0
LEAN: Light and Efficient Audio Classification Network—0
LEAPER: Fast and Accurate FPGA-based System Performance Prediction via Transfer Learning—0
LEAPME: Learning-based Property Matching with Embeddings—0
Learn and Transfer Knowledge of Preferred Assistance Strategies in Semi-autonomous Telemanipulation—0
Synthetic Data Can Also Teach: Synthesizing Effective Data for Unsupervised Visual Representation Learning—0
Learn Dynamic-Aware State Embedding for Transfer Learning—0
Learned 3D Shape Representations Using Fused Geometrically Augmented Images: Application to Facial Expression and Action Unit Detection—0
Learn Faster and Forget Slower via Fast and Stable Task Adaptation—0
Learn from Balance: Rectifying Knowledge Transfer for Long-Tailed Scenarios—0
Learn From the Past: Experience Ensemble Knowledge Distillation—0
Learning 3D Robotics Perception using Inductive Priors—0
Learning 4D Panoptic Scene Graph Generation from Rich 2D Visual Scene—0
Learning Abstract Concept Embeddings from Multi-Modal Data: Since You Probably Can't See What I Mean—0
Learning across label confidence distributions using Filtered Transfer Learning—0
Learning a Deep Compact Image Representation for Visual Tracking—0
Learning a Deep Model for Human Action Recognition from Novel Viewpoints—0
Learning a functional control for high-frequency finance—0
Robust and flexible learning of a high-dimensional classification rule using auxiliary outcomes—0
Learning and Knowledge Transfer with Memory Networks for Machine Comprehension—0
Learning a Non-Linear Knowledge Transfer Model for Cross-View Action Recognition—0
Learning Answer Embeddings for Visual Question Answering—0
Learning ASR pathways: A sparse multilingual ASR model—0
Learning Attentive Meta-Transfer—0
Learning bilingual word embeddings with (almost) no bilingual data—0
Learning Bound for Parameter Transfer Learning—0
Learning compact generalizable neural representations supporting perceptual grouping—0
Learning Cooperation and Online Planning Through Simulation and Graph Convolutional Network—0
Learning Cross-lingual Representations for Event Coreference Resolution with Multi-view Alignment and Optimal Transport—0
Learning Curves for Continual Learning in Neural Networks: Self-Knowledge Transfer and Forgetting—0
Learning Deep Representations via Contrastive Learning for Instance Retrieval—0
Learning Deep Representations with Probabilistic Knowledge Transfer—0
InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees—0
Learning Causal Domain-Invariant Temporal Dynamics for Few-Shot Action Recognition—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