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Found 40 papers, 20 papers with code
Domain Adaptation for Learned Image Compression with Supervised Adapters
In Learned Image Compression (LIC), a model is trained at encoding and decoding images sampled from a source domain, often outperforming traditional codecs on natural images; yet its performance may be far from optimal on images sampled from different domains.
Debiasing surgeon: fantastic weights and how to find them
Nowadays an ever-growing concerning phenomenon, the emergence of algorithmic biases that can lead to unfair models, emerges.
Find the Lady: Permutation and Re-Synchronization of Deep Neural Networks
We advance a method to re-synchronize the order of permuted neurons.
SCoTTi: Save Computation at Training Time with an adaptive framework
On-device training is an emerging approach in machine learning where models are trained on edge devices, aiming to enhance privacy protection and real-time performance.
Enhanced EEG-Based Mental State Classification : A novel approach to eliminate data leakage and improve training optimization for Machine Learning
In this paper, we explore prior research and introduce a new methodology for classifying mental state levels based on EEG signals utilizing machine learning (ML).
Weighted Ensemble Models Are Strong Continual Learners
In this work, we study the problem of continual learning (CL) where the goal is to learn a model on a sequence of tasks, such that the data from the previous tasks becomes unavailable while learning on the current task data.
Towards On-device Learning on the Edge: Ways to Select Neurons to Update under a Budget Constraint
In the realm of efficient on-device learning under extreme memory and computation constraints, a significant gap in successful approaches persists.
Mini but Mighty: Finetuning ViTs with Mini Adapters
Vision Transformers (ViTs) have become one of the dominant architectures in computer vision, and pre-trained ViT models are commonly adapted to new tasks via fine-tuning.
Rethinking Class-incremental Learning in the Era of Large Pre-trained Models via Test-Time Adaptation
However, repeated fine-tuning on each task destroys the rich representations of the PTMs and further leads to forgetting previous tasks.
Can Unstructured Pruning Reduce the Depth in Deep Neural Networks?
Pruning is a widely used technique for reducing the size of deep neural networks while maintaining their performance.
Sparse Double Descent in Vision Transformers: real or phantom threat?
Vision transformers (ViT) have been of broad interest in recent theoretical and empirical works.
Mining bias-target Alignment from Voronoi Cells
Despite significant research efforts, deep neural networks are still vulnerable to biases: this raises concerns about their fairness and limits their generalization.
Learn how to Prune Pixels for Multi-view Neural Image-based Synthesis
Image-based rendering techniques stand at the core of an immersive experience for the user, as they generate novel views given a set of multiple input images.
Optimized preprocessing and Tiny ML for Attention State Classification
In this paper, we present a new approach to mental state classification from EEG signals by combining signal processing techniques and machine learning (ML) algorithms.
DSD$^2$: Can We Dodge Sparse Double Descent and Compress the Neural Network Worry-Free?
Second, we introduce an entropy measure providing more insights into the insurgence of this phenomenon and enabling the use of traditional stop criteria.
Can we avoid Double Descent in Deep Neural Networks?
Very recently, an unexpected phenomenon, the ``double descent'', has caught the attention of the deep learning community.
Unbiased Supervised Contrastive Learning
In this work, we tackle the problem of learning representations that are robust to biases.
Compressing Explicit Voxel Grid Representations: fast NeRFs become also small
NeRFs have revolutionized the world of per-scene radiance field reconstruction because of their intrinsic compactness.
Packed-Ensembles for Efficient Uncertainty Estimation
Deep Ensembles (DE) are a prominent approach for achieving excellent performance on key metrics such as accuracy, calibration, uncertainty estimation, and out-of-distribution detection.
Information Removal at the bottleneck in Deep Neural Networks
Deep learning models are nowadays broadly deployed to solve an incredibly large variety of tasks.
UniToBrain dataset: a Brain Perfusion Dataset
The CT perfusion (CTP) is a medical exam for measuring the passage of a bolus of contrast solution through the brain on a pixel-by-pixel basis.
To update or not to update? Neurons at equilibrium in deep models
Recent advances in deep learning optimization showed that, with some a-posteriori information on fully-trained models, it is possible to match the same performance by simply training a subset of their parameters.
Disentangling private classes through regularization
In this work we propose DisP, an approach for deep learning models disentangling the information related to some classes we desire to keep private, from the data processed by AI.
Unsupervised Learning of Unbiased Visual Representations
We propose a fully unsupervised debiasing framework, consisting of three steps: first, we exploit the natural preference for learning malignant biases, obtaining a bias-capturing model; then, we perform a pseudo-labelling step to obtain bias labels; finally we employ state-of-the-art supervised debiasing techniques to obtain an unbiased model.
REM: Routing Entropy Minimization for Capsule Networks
This paper proposes REM, a technique which minimizes the entropy of the parse tree-like structure, improving its explainability.
The rise of the lottery heroes: why zero-shot pruning is hard
Recent advances in deep learning optimization showed that just a subset of parameters are really necessary to successfully train a model.
HEMP: High-order Entropy Minimization for neural network comPression
We formulate the entropy of a quantized artificial neural network as a differentiable function that can be plugged as a regularization term into the cost function minimized by gradient descent.
EnD: Entangling and Disentangling deep representations for bias correction
Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, and nowadays they are used to solve an incredibly large variety of tasks.
Ranked #1 on
HairColor/Unbiased
on CelebA
Dysplasia grading of colorectal polyps through CNN analysis of WSI
Colorectal cancer is a leading cause of cancer death for both men and women.
SeReNe: Sensitivity based Regularization of Neurons for Structured Sparsity in Neural Networks
Deep neural networks include millions of learnable parameters, making their deployment over resource-constrained devices problematic.
UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading
Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma.
Ranked #1 on
Colorectal Polyps Characterization
on UNITOPATHO
Colorectal Polyps Characterization
General Classification
+3
A two-step explainable approach for COVID-19 computer-aided diagnosis from chest x-ray images
Early screening of patients is a critical issue in order to assess immediate and fast responses against the spread of COVID-19.
Neural Network-derived perfusion maps: a Model-free approach to computed tomography perfusion in patients with acute ischemic stroke
Methods: Training of the CNN was done on a subset of 100 perfusion data, while 15 samples were used as validation.
LOss-Based SensiTivity rEgulaRization: towards deep sparse neural networks
LOBSTER (LOss-Based SensiTivity rEgulaRization) is a method for training neural networks having a sparse topology.
A non-discriminatory approach to ethical deep learning
Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, nowadays they are used to solve an incredibly large variety of tasks.
Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima
Recently, a race towards the simplification of deep networks has begun, showing that it is effectively possible to reduce the size of these models with minimal or no performance loss.
Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community.
Post-synaptic potential regularization has potential
Improving generalization is one of the main challenges for training deep neural networks on classification tasks.
Learning Sparse Neural Networks via Sensitivity-Driven Regularization
The ever-increasing number of parameters in deep neural networks poses challenges for memory-limited applications.
On the role of synaptic stochasticity in training low-precision neural networks
Stochasticity and limited precision of synaptic weights in neural network models are key aspects of both biological and hardware modeling of learning processes.
