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 1951–1975 of 10307 papers

TitleStatusHype
CADE: Cosine Annealing Differential Evolution for Spiking Neural NetworkCode0
HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationCode0
GYM at Qur’an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language ModelsCode0
Hacking Task Confounder in Meta-LearningCode0
HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of ActionsCode0
Harnessing multiple LLMs for Information Retrieval: A case study on Deep Learning methodologies in Biodiversity publicationsCode0
Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson RegressionCode0
Growing Neural Network with Shared ParameterCode0
Group-level Emotion Recognition using Transfer Learning from Face IdentificationCode0
GTNet: Generative Transfer Network for Zero-Shot Object DetectionCode0
Graph Neural Networks for Surfactant Multi-Property PredictionCode0
Comparative Analysis of Pretrained Audio Representations in Music Recommender SystemsCode0
Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation ModelsCode0
Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative StudyCode0
Comparative Analysis: Violence Recognition from Videos using Transfer LearningCode0
Comparative evaluation of CNN architectures for Image Caption GenerationCode0
Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation SystemsCode0
Graph Few-shot Learning via Knowledge TransferCode0
Building an Endangered Language Resource in the Classroom: Universal Dependencies for KakataiboCode0
GreekBART: The First Pretrained Greek Sequence-to-Sequence ModelCode0
Guided Transfer LearningCode0
Graph-based Knowledge Distillation by Multi-head Attention NetworkCode0
ANNA: Abstractive Text-to-Image Synthesis with Filtered News CaptionsCode0
GraphBridge: Towards Arbitrary Transfer Learning in GNNsCode0
An LSTM Feature Imitation Network for Hand Movement Recognition from sEMG SignalsCode0
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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