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Found 16 papers, 12 papers with code
Autonomous Quadrotor Landing using Deep Reinforcement Learning
Landing an unmanned aerial vehicle (UAV) on a ground marker is an open problem despite the effort of the research community.
Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers
Group emotion recognition in the wild is a challenging problem, due to the unstructured environments in which everyday life pictures are taken.
Y-Autoencoders: disentangling latent representations via sequential-encoding
Additionally, the projection in the explicit manifold is monitored by a predictor, that is embedded in the encoder and trained end-to-end with no adversarial losses.
Sample-efficient Deep Reinforcement Learning with Imaginary Rollouts for Human-Robot Interaction
Deep reinforcement learning has proven to be a great success in allowing agents to learn complex tasks.
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
Recently, different machine learning methods have been introduced to tackle the challenging few-shot learning scenario that is, learning from a small labeled dataset related to a specific task.
Defining Benchmarks for Continual Few-Shot Learning
Both few-shot and continual learning have seen substantial progress in the last years due to the introduction of proper benchmarks.
Self-Supervised Relational Reasoning for Representation Learning
In self-supervised learning, a system is tasked with achieving a surrogate objective by defining alternative targets on a set of unlabeled data.
Class Imbalance in Few-Shot Learning
Few-shot learning aims to train models on a limited number of labeled samples from a support set in order to generalize to unseen samples from a query set.
Few-Shot Learning with Class Imbalance
Few-Shot Learning (FSL) algorithms are commonly trained through Meta-Learning (ML), which exposes models to batches of tasks sampled from a meta-dataset to mimic tasks seen during evaluation.
How Sensitive are Meta-Learners to Dataset Imbalance?
Meta-Learning (ML) has proven to be a useful tool for training Few-Shot Learning (FSL) algorithms by exposure to batches of tasks sampled from a meta-dataset.
Memory Efficient Meta-Learning with Large Images
This limitation arises because a task's entire support set, which can contain up to 1000 images, must be processed before an optimization step can be taken.
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).
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification
Modern deep learning systems are increasingly deployed in situations such as personalization and federated learning where it is necessary to support i) learning on small amounts of data, and ii) communication efficient distributed training protocols.
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification
In this paper we push this Pareto frontier in the few-shot image classification setting with a key contribution: a new adaptive block called Contextual Squeeze-and-Excitation (CaSE) that adjusts a pretrained neural network on a new task to significantly improve performance with a single forward pass of the user data (context).
Ranked #3 on
Few-Shot Image Classification
on Meta-Dataset
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
Despite its simplicity this baseline is competitive with meta-learning methods on a variety of conditions and is able to imitate target policies trained on unseen variations of the original environment.
Few-Shot Image Classification
Few-Shot Imitation Learning
+3
Transformer Neural Autoregressive Flows
In this paper, we propose a novel solution to these challenges by exploiting transformers to define a new class of neural flows called Transformer Neural Autoregressive Flows (T-NAFs).
