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Found 6 papers, 3 papers with code
CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation
In this paper, we propose a simple Contrastive Learning framework for semi-supervised Domain Adaptation (CLDA) that attempts to bridge the intra-domain gap between the labeled and unlabeled target distributions and inter-domain gap between source and unlabeled target distribution in SSDA.
Semi-Supervised Action Recognition with Temporal Contrastive Learning
We approach this problem by learning a two-pathway temporal contrastive model using unlabeled videos at two different speeds leveraging the fact that changing video speed does not change an action.
Emergence of cooperatively reorganizing cluster and super-Arrhenius dynamics of fragile supercooled liquids
It is shown that the value of {\psi}(T ) is equal to 1 for T > T a indicating that the underlying microscopic mechanism of relaxation is dominated by the entropy driven processes while for T < T a , {\psi}(T ) decreases on cooling indicating the emergence of the energy driven processes.
Soft Condensed Matter
Mitigating Dataset Imbalance via Joint Generation and Classification
In this work we address these questions from the perspective of dataset imbalance resulting out of severe under-representation of annotated training data for certain classes and its effect on both deep classification and generation methods.
ParallelDots at SemEval-2019 Task 3: Domain Adaptation with feature embeddings for Contextual Emotion Analysis
This paper describes our proposed system {\&} experiments performed to detect contextual emotion in texts for SemEval 2019 Task 3.
BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification
Deep learning based landcover classification algorithms have recently been proposed in literature.
Ranked #12 on
Hyperspectral Image Classification
on Indian Pines
(Overall Accuracy metric)
