Search Results for author:
Found 19 papers, 7 papers with code
Make Me Happier: Evoking Emotions Through Image Diffusion Models
For the first time, we present a novel challenge of emotion-evoked image generation, aiming to synthesize images that evoke target emotions while retaining the semantics and structures of the original scenes.
LIST: Learning to Index Spatio-Textual Data for Embedding based Spatial Keyword Queries
With the proliferation of spatio-textual data, Top-k KNN spatial keyword queries (TkQs), which return a list of objects based on a ranking function that evaluates both spatial and textual relevance, have found many real-life applications.
MosaicFusion: Diffusion Models as Data Augmenters for Large Vocabulary Instance Segmentation
We present MosaicFusion, a simple yet effective diffusion-based data augmentation approach for large vocabulary instance segmentation.
Towards Building Voice-based Conversational Recommender Systems: Datasets, Potential Solutions, and Prospects
Conversational recommender systems (CRSs) have become crucial emerging research topics in the field of RSs, thanks to their natural advantages of explicitly acquiring user preferences via interactive conversations and revealing the reasons behind recommendations.
Unfolded Self-Reconstruction LSH: Towards Machine Unlearning in Approximate Nearest Neighbour Search
This need requires the ANN search algorithm to support fast online data deletion and insertion.
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge
In future, learning on small data that approximates the generalization ability of big data is one of the ultimate purposes of AI, which requires machines to recognize objectives and scenarios relying on small data as humans.
Masked Frequency Modeling for Self-Supervised Visual Pre-Training
We present Masked Frequency Modeling (MFM), a unified frequency-domain-based approach for self-supervised pre-training of visual models.
Unsupervised Object-Level Representation Learning from Scene Images
Extensive experiments on COCO show that ORL significantly improves the performance of self-supervised learning on scene images, even surpassing supervised ImageNet pre-training on several downstream tasks.
An Improved Transfer Model: Randomized Transferable Machine
Existing studies usually assume that the learned new feature representation is \emph{domain-invariant}, and thus train a transfer model $\mathcal{M}$ on the source domain.
Delving into Inter-Image Invariance for Unsupervised Visual Representations
In this work, we present a comprehensive empirical study to better understand the role of inter-image invariance learning from three main constituting components: pseudo-label maintenance, sampling strategy, and decision boundary design.
Online Deep Clustering for Unsupervised Representation Learning
In this way, labels and the network evolve shoulder-to-shoulder rather than alternatingly.
Metagross: Meta Gated Recursive Controller Units for Sequence Modeling
This paper proposes Metagross (Meta Gated Recursive Controller), a new neural sequence modeling unit.
Jacobian Adversarially Regularized Networks for Robustness
Adversarial examples are crafted with imperceptible perturbations with the intent to fool neural networks.
Automatic Construction of Multi-layer Perceptron Network from Streaming Examples
The advantage of NADINE, namely elastic structure and online learning trait, is numerically validated using nine data stream classification and regression problems where it demonstrates performance improvement over prominent algorithms in all problems.
DEVDAN: Deep Evolving Denoising Autoencoder
The Denoising Autoencoder (DAE) enhances the flexibility of the data stream method in exploiting unlabeled samples.
Autonomous Deep Learning: Incremental Learning of Denoising Autoencoder for Evolving Data Streams
The generative learning phase of Autoencoder (AE) and its successor Denosing Autoencoder (DAE) enhances the flexibility of data stream method in exploiting unlabelled samples.
Metamorphic Relation Based Adversarial Attacks on Differentiable Neural Computer
Deep neural networks (DNN), while becoming the driving force of many novel technology and achieving tremendous success in many cutting-edge applications, are still vulnerable to adversarial attacks.
Addressing Expensive Multi-objective Games with Postponed Preference Articulation via Memetic Co-evolution
This paper presents algorithmic and empirical contributions demonstrating that the convergence characteristics of a co-evolutionary approach to tackle Multi-Objective Games (MOGs) with postponed preference articulation can often be hampered due to the possible emergence of the so-called Red Queen effect.
MIML-FCN+: Multi-instance Multi-label Learning via Fully Convolutional Networks with Privileged Information
As the proposed PI loss is convex and SGD compatible and the framework itself is a fully convolutional network, MIML-FCN+ can be easily integrated with state of-the-art deep learning networks.
