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Found 15 papers, 4 papers with code
Domain Adaptation Using Pseudo Labels
In the absence of labeled target data, unsupervised domain adaptation approaches seek to align the marginal distributions of the source and target domains in order to train a classifier for the target.
Domain-Invariant Feature Alignment Using Variational Inference For Partial Domain Adaptation
The standard closed-set domain adaptation approaches seek to mitigate distribution discrepancies between two domains under the constraint of both sharing identical label sets.
PatchRot: A Self-Supervised Technique for Training Vision Transformers
Vision transformers require a huge amount of labeled data to outperform convolutional neural networks.
Coupling Adversarial Learning with Selective Voting Strategy for Distribution Alignment in Partial Domain Adaptation
In contrast to a standard closed-set domain adaptation task, partial domain adaptation setup caters to a realistic scenario by relaxing the identical label set assumption.
Sparsity Regularization For Cold-Start Recommendation
Recently, Generative Adversarial Networks (GANs) have been applied to the problem of Cold-Start Recommendation, but the training performance of these models is hampered by the extreme sparsity in warm user purchase behavior.
Partial Domain Adaptation Using Selective Representation Learning For Class-Weight Computation
Driven by the motivation that image styles are private to each domain, in this work, we develop a method that identifies outlier classes exclusively from image content information and train a label classifier exclusively on class-content from source images.
Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learning
The SR-loss guides the LsrGAN to generate visual features that mirror the semantic relationships between seen and unseen classes.
Representation, Exploration and Recommendation of Music Playlists
Playlists have become a significant part of our listening experience because of the digital cloud-based services such as Spotify, Pandora, Apple Music.
Efficient Approximate Solutions to Mutual Information Based Global Feature Selection
Estimating the MI for a subset of features is often intractable.
Multiresolution Match Kernels for Gesture Video Classification
The emergence of depth imaging technologies like the Microsoft Kinect has renewed interest in computational methods for gesture classification based on videos.
Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation
To determine the parameters in the NET model (and in other unsupervised domain adaptation models), we introduce a validation procedure by sampling source data points that are similar in distribution to the target data.
Coupled Support Vector Machines for Supervised Domain Adaptation
Popular domain adaptation (DA) techniques learn a classifier for the target domain by sampling relevant data points from the source and combining it with the target data.
Deep Hashing Network for Unsupervised Domain Adaptation
Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain.
Nonlinear Embedding Transform for Unsupervised Domain Adaptation
The problem of domain adaptation (DA) deals with adapting classifier models trained on one data distribution to different data distributions.
A Strategy for an Uncompromising Incremental Learner
Using an implementation based on deep neural networks, we demonstrate that phantom sampling dramatically avoids catastrophic forgetting.
