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Found 45 papers, 23 papers with code
The Tunnel Effect: Building Data Representations in Deep Neural Networks
Deep neural networks are widely known for their remarkable effectiveness across various tasks, with the consensus that deeper networks implicitly learn more complex data representations.
BlendFields: Few-Shot Example-Driven Facial Modeling
Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance.
Exploring Continual Learning of Diffusion Models
Diffusion models have achieved remarkable success in generating high-quality images thanks to their novel training procedures applied to unprecedented amounts of data.
Active Visual Exploration Based on Attention-Map Entropy
Active visual exploration addresses the issue of limited sensor capabilities in real-world scenarios, where successive observations are actively chosen based on the environment.
Hypernetworks build Implicit Neural Representations of Sounds
Implicit Neural Representations (INRs) are nowadays used to represent multimedia signals across various real-life applications, including image super-resolution, image compression, or 3D rendering.
Can gamification reduce the burden of self-reporting in mHealth applications? A feasibility study using machine learning from smartwatch data to estimate cognitive load
The effectiveness of digital treatments can be measured by requiring patients to self-report their state through applications, however, it can be overwhelming and causes disengagement.
Towards Unsupervised Visual Reasoning: Do Off-The-Shelf Features Know How to Reason?
Recent advances in visual representation learning allowed to build an abundance of powerful off-the-shelf features that are ready-to-use for numerous downstream tasks.
Emergency action termination for immediate reaction in hierarchical reinforcement learning
In this paper, we address this gap in the state-of-the-art approaches and propose a method in which the validity of higher-level actions (thus lower-level goals) is constantly verified at the higher level.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks
Implicit neural representations (INRs) are a rapidly growing research field, which provides alternative ways to represent multimedia signals.
Selectively increasing the diversity of GAN-generated samples
Especially prone to mode collapse are conditional GANs, which tend to ignore the input noise vector and focus on the conditional information.
Progressive Latent Replay for efficient Generative Rehearsal
We introduce a new method for internal replay that modulates the frequency of rehearsal based on the depth of the network.
Continual Learning with Guarantees via Weight Interval Constraints
We introduce a new training paradigm that enforces interval constraints on neural network parameter space to control forgetting.
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models
Their main strength comes from their unique setup in which a model (the backward diffusion process) is trained to reverse the forward diffusion process, which gradually adds noise to the input signal.
Deep Learning Fetal Ultrasound Video Model Match Human Observers in Biometric Measurements
We found that automated fetal biometric measurements obtained by FUVAI were comparable to the measurements performed by experienced sonographers The observed differences in measurement values were within the range of inter- and intra-observer variability.
BabyNet: Residual Transformer Module for Birth Weight Prediction on Fetal Ultrasound Video
Predicting fetal weight at birth is an important aspect of perinatal care, particularly in the context of antenatal management, which includes the planned timing and the mode of delivery.
Continual learning on 3D point clouds with random compressed rehearsal
By using rehearsal and reconstruction as regularization methods of the learning process, our approach achieves a significant decrease of catastrophic forgetting compared to the existing solutions on several most popular point cloud datasets considering two continual learning settings: when a task is known beforehand, and in the challenging scenario of when task information is unknown to the model.
POTHER: Patch-Voted Deep Learning-Based Chest X-ray Bias Analysis for COVID-19 Detection
We show that our proposed method is more robust than previous attempts to counter confounding factors such as ECG leads in chest X-rays that often influence model classification decisions.
Logarithmic Continual Learning
Our approach leverages allocation of past data in a~set of generative models such that most of them do not require retraining after a~task.
CoNeRF: Controllable Neural Radiance Fields
We extend neural 3D representations to allow for intuitive and interpretable user control beyond novel view rendering (i. e. camera control).
Non-Gaussian Gaussian Processes for Few-Shot Regression
This makes the GP posterior locally non-Gaussian, therefore we name our method Non-Gaussian Gaussian Processes (NGGPs).
HyperCube: Implicit Field Representations of Voxelized 3D Models
Recently introduced implicit field representations offer an effective way of generating 3D object shapes.
Efficient GPU implementation of randomized SVD and its applications
In this work, we leverage efficient processing operations that can be run in parallel on modern Graphical Processing Units (GPUs), predominant computing architecture used e. g. in deep learning, to reduce the computational burden of computing matrix decompositions.
On robustness of generative representations against catastrophic forgetting
Catastrophic forgetting of previously learned knowledge while learning new tasks is a widely observed limitation of contemporary neural networks.
HyperColor: A HyperNetwork Approach for Synthesizing Auto-colored 3D Models for Game Scenes Population
In such a case, we have to ``understand'' the object's composition and coloring scheme of each part.
FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements
The main goal in fetal ultrasound scan video analysis is to find proper standard planes to measure the fetal head, abdomen and femur.
Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual Learning
We propose a new method for unsupervised generative continual learning through realignment of Variational Autoencoder's latent space.
Visual Probing: Cognitive Framework for Explaining Self-Supervised Image Representations
Motivated by this observation, we introduce a novel visual probing framework for explaining the self-supervised models by leveraging probing tasks employed previously in natural language processing.
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks
The problem of reducing processing time of large deep learning models is a fundamental challenge in many real-world applications.
Convolutional Neural Networks in Orthodontics: a review
Convolutional neural networks (CNNs) are used in many areas of computer vision, such as object tracking and recognition, security, military, and biomedical image analysis.
TrajeVAE: Controllable Human Motion Generation from Trajectories
The creation of plausible and controllable 3D human motion animations is a long-standing problem that requires a manual intervention of skilled artists.
HyperPocket: Generative Point Cloud Completion
In this work, we reformulate the problem of point cloud completion into an object hallucination task.
Modeling 3D Surface Manifolds with a Locally Conditioned Atlas
This way, we can sample a mesh quad on that sphere and project it back onto the object's manifold.
BinPlay: A Binary Latent Autoencoder for Generative Replay Continual Learning
We introduce a binary latent space autoencoder architecture to rehearse training samples for the continual learning of neural networks.
Representing Point Clouds with Generative Conditional Invertible Flow Networks
To exploit similarities between same-class objects and to improve model performance, we turn to weight sharing: networks that model densities of points belonging to objects in the same family share all parameters with the exception of a small, object-specific embedding vector.
Spontaneous preterm birth prediction using convolutional neural networks
Based on the conducted results and model efficiency, we decided to extend U-Net by adding a parallel branch for classification task.
HyperFlow: Representing 3D Objects as Surfaces
To that end, we devise a generative model that uses a hypernetwork to return the weights of a Continuous Normalizing Flows (CNF) target network.
End-to-end Sinkhorn Autoencoder with Noise Generator
To address these shortcomings, we introduce a novel method dubbed end-to-end Sinkhorn Autoencoder, that leverages sinkhorn algorithm to explicitly align distribution of encoded real data examples and generated noise.
Understanding the robustness of deep neural network classifiers for breast cancer screening
Deep neural networks (DNNs) show promise in breast cancer screening, but their robustness to input perturbations must be better understood before they can be clinically implemented.
Hypernetwork approach to generating point clouds
The main idea of our HyperCloud method is to build a hyper network that returns weights of a particular neural network (target network) trained to map points from a uniform unit ball distribution into a 3D shape.
Estimation of preterm birth markers with U-Net segmentation network
To achieve this goal, we propose to first use a deep neural network architecture for segmenting prenatal ultrasound images and then automatically extract two biophysical ultrasound markers, cervical length (CL) and anterior cervical angle (ACA), from the resulting images.
Comixify: Transform video into a comics
In this paper, we propose a solution to transform a video into a comics.
Adversarial Autoencoders for Compact Representations of 3D Point Clouds
Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds.
Neural Comic Style Transfer: Case Study
The work by Gatys et al. [1] recently showed a neural style algorithm that can produce an image in the style of another image.
Speaker Diarization using Deep Recurrent Convolutional Neural Networks for Speaker Embeddings
In this paper we propose a new method of speaker diarization that employs a deep learning architecture to learn speaker embeddings.
What Looks Good with my Sofa: Multimodal Search Engine for Interior Design
In this paper, we propose a multi-modal search engine for interior design that combines visual and textual queries.
