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Found 15 papers, 3 papers with code
AUTOKD: Automatic Knowledge Distillation Into A Student Architecture Family
State-of-the-art results in deep learning have been improving steadily, in good part due to the use of larger models.
OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression
To eliminate the requirement of saving separate models for different target datasets, we propose a novel setting that starts from a pretrained deep generative model and compresses the data batches while adapting the model with a dynamical system for only one epoch.
Adversarial Branch Architecture Search for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA) is a key issue in visual recognition, as it allows to bridge different visual domains enabling robust performances in the real world.
Don't be picky, all students in the right family can learn from good teachers
State-of-the-art results in deep learning have been improving steadily, in good part due to the use of larger models.
Self-Supervised Learning Across Domains
Human adaptability relies crucially on learning and merging knowledge from both supervised and unsupervised tasks: the parents point out few important concepts, but then the children fill in the gaps on their own.
Ranked #79 on
Domain Generalization
on PACS
MANAS: Multi-Agent Neural Architecture Search
The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a graph-level global objective.
Domain Generalization by Solving Jigsaw Puzzles
Human adaptability relies crucially on the ability to learn and merge knowledge both from supervised and unsupervised learning: the parents point out few important concepts, but then the children fill in the gaps on their own.
Ranked #3 on
Domain Generalization
on NICO Animal
Learning to see across Domains and Modalities
This thesis will focus on a family of transfer learning methods applied to the task of visual object recognition, specifically image classification.
From source to target and back: symmetric bi-directional adaptive GAN
The effectiveness of generative adversarial approaches in producing images according to a specific style or visual domain has recently opened new directions to solve the unsupervised domain adaptation problem.
Ranked #13 on
Domain Adaptation
on SVHN-to-MNIST
Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition
Despite the impressive progress brought by deep network in visual object recognition, robot vision is still far from being a solved problem.
AutoDIAL: Automatic DomaIn Alignment Layers
Here we take a different route, proposing to align the learned representations by embedding in any given network specific Domain Alignment Layers, designed to match the source and target feature distributions to a reference one.
Just DIAL: DomaIn Alignment Layers for Unsupervised Domain Adaptation
The empirical fact that classifiers, trained on given data collections, perform poorly when tested on data acquired in different settings is theoretically explained in domain adaptation through a shift among distributions of the source and target domains.
A deep representation for depth images from synthetic data
We show that the filters learned from such data collection, using the very same architecture typically used on visual data, learns very different filters, resulting in depth features (a) able to better characterize the different facets of depth images, and (b) complementary with respect to those derived from CNNs pre-trained on 2D datasets.
When Naive Bayes Nearest Neighbors Meet Convolutional Neural Networks
Since Convolutional Neural Networks (CNNs) have become the leading learning paradigm in visual recognition, Naive Bayes Nearest Neighbor (NBNN)-based classifiers have lost momentum in the community.
When Naïve Bayes Nearest Neighbours Meet Convolutional Neural Networks
Since Convolutional Neural Networks (CNNs) have become the leading learning paradigm in visual recognition, Naive Bayes Nearest Neighbour (NBNN)-based classifiers have lost momentum in the community.
