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 4326–4350 of 10307 papers

TitleStatusHype
A P300 BCI for the Masses: Prior Information Enables Instant Unsupervised Spelling—0
Adversarial Inductive Transfer Learning with input and output space adaptation—0
Action Recognition for American Sign Language—0
Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning—0
Assessing the Performance of Analog Training for Transfer Learning—0
Emotion Recognition Using Fusion of Audio and Video Features—0
Conversational Transfer Learning for Emotion Recognition—0
Emotion Detection from EEG using Transfer Learning—0
Cell Selection with Deep Reinforcement Learning in Sparse Mobile Crowdsensing—0
AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images—0
Emotion Classification in Short English Texts using Deep Learning Techniques—0
Emotion Classification in Low and Moderate Resource Languages—0
CellLineNet: End-to-End Learning and Transfer Learning For Multiclass Epithelial Breast cell Line Classification via a Convolutional Neural Network—0
AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors—0
Adversarial Imitation via Variational Inverse Reinforcement Learning—0
Pay attention to emoji: Feature Fusion Network with EmoGraph2vec Model for Sentiment Analysis—0
EMGTTL: Transformers-Based Transfer Learning for Classification of ADL using Raw Surface EMG Signals—0
EMGTFNet: Fuzzy Vision Transformer to decode Upperlimb sEMG signals for Hand Gestures Recognition—0
CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection—0
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning—0
CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks—0
Any-Shot Sequential Anomaly Detection in Surveillance Videos—0
Adversarial Fine-tune with Dynamically Regulated Adversary—0
Embodied Multimodal Multitask Learning—0
Embed Everything: A Method for Efficiently Co-Embedding Multi-Modal Spaces—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