DeepNC: Deep Generative Network Completion

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Current machine learning models for vision are often highly specialized and limited to a single modality and task. In contrast, recent large language models exhibit a wide range of capabilities, hinting at a possibility for similarly versatile models in co ...

We have developed a new tool that makes it possible for people with zero programming experience to intentionally and meaningfully explore the latent space of a GAN. We combine a number of methods from the literature into a single system that includes multi ...

Classic image-restoration algorithms use a variety of priors, either implicitly or explicitly. Their priors are hand-designed and their corresponding weights are heuristically assigned. Hence, deep learning methods often produce superior image restoration ...

Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain. To tackle these issues, this paper proposes a novel Tes ...

Data-driven and model-driven methodologies can be regarded as competitive fields since they tackle similar problems such as prediction. However, these two fields can learn from each other to improve themselves. Indeed, data-driven methodologies have been d ...

Recent transformer language models achieve outstanding results in many natural language processing (NLP) tasks. However, their enormous size often makes them impractical on memory-constrained devices, requiring practitioners to compress them to smaller net ...

Deep neural networks have achieved impressive results in many image classification tasks. However, since their performance is usually measured in controlled settings, it is important to ensure that their decisions remain correct when deployed in noisy envi ...

Abstractive summarization has seen big improvements in recent years, mostly due to advances in neural language modeling, language model pretraining, and scaling models and datasets. While large language models generate summaries that are fluent, coherent, ...

Flow-based generative models have become an important class of unsupervised learning approaches. In this work, we incorporate the key ideas of renormalization group (RG) and sparse prior distribution to design a hierarchical flow-based generative model, RG ...

The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectr ...

State-of-the-art (SOTA) face recognition systems generally use deep convolutional neural networks (CNNs) to extract deep features, called embeddings, from face images. The face embeddings are stored in the system's database and are used for recognition of ...

This paper investigates the potential impact of deep generative models on the work of creative professionals, specifically focusing on fashion design. We argue that current generative modeling tools lack critical features that would make them useful creati ...

According to the proposed Artificial Intelligence Act by the European Comission (expected to pass at the end of 2023), the class of High-Risk AI Systems (Title III) comprises several important applications of Deep Learning like autonomous driving vehicles ...

Recent developments in network neuroscience have highlighted the importance of developing techniques for analysing and modelling brain networks. A particularly powerful approach for studying complex neural systems is to formulate generative models that use ...

Remote sensing visual question answering (RQA) was recently proposed with the aim of interfacing natural language and vision to ease the access of information contained in Earth Observation data for a wide audience, which is granted by simple questions in ...

Random Fourier features (RFFs) provide a promising way for kernel learning in a spectral case. Current RFFs-based kernel learning methods usually work in a two-stage way. In the first-stage process, learn-ing an optimal feature map is often formulated as a ...

DeepNC: Deep Generative Network Completion

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4M: Massively Multimodal Masked Modeling

GANs for All: Supporting Fun and Intuitive Exploration of GAN Latent Spaces

BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Bridging the gap between model-driven and data-driven methods in the era of Big Data

Revisiting Offline Compression: Going Beyond Factorization-based Methods for Transformer Language Models

Robustness and invariance properties of image classifiers

Interpretable Representation Learning and Evaluation for Abstractive Summarization

RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

Face Reconstruction Fromdeep Facial Embeddings Using A Convolutional Neural Network

Fashioning the Future: Unlocking the Creative Potential of Deep Generative Models for Design Space Exploration

Robust Training and Verification of Deep Neural Networks

Investigating cognitive ability using action-based models of structural brain networks

Prompt–RSVQA: Prompting visual context to a language model for Remote Sensing Visual Question Answering

End-to-end kernel learning via generative random Fourier features