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Found 17 papers, 8 papers with code
Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models
First, we introduce a scenario where the embedding of a pre-trained model is served through a gated API with compute-cost and data-privacy constraints.
Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data
Under unconfoundedness, we target the Individual Treatment Effect (ITE) estimation with unobserved heterogeneity in the treatment response due to missing risk factors.
Open-Set Likelihood Maximization for Few-Shot Learning
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i. e. classifying instances among a set of classes for which we only have a few labeled samples, while simultaneously detecting instances that do not belong to any known class.
Model-Agnostic Few-Shot Open-Set Recognition
Through extensive experiments spanning 5 datasets, we show that OSTIM surpasses both inductive and existing transductive methods in detecting open-set instances while competing with the strongest transductive methods in classifying closed-set instances.
Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task Sampling
Every day, a new method is published to tackle Few-Shot Image Classification, showing better and better performances on academic benchmarks.
StreaMulT: Streaming Multimodal Transformer for Heterogeneous and Arbitrary Long Sequential Data
The increasing complexity of Industry 4. 0 systems brings new challenges regarding predictive maintenance tasks such as fault detection and diagnosis.
Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift
To classify query instances from novel classes encountered at test-time, they only require a support set composed of a few labelled samples.
Spatial Contrastive Learning for Few-Shot Classification
In this paper, we explore contrastive learning for few-shot classification, in which we propose to use it as an additional auxiliary training objective acting as a data-dependent regularizer to promote more general and transferable features.
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation
First, we select for annotation target samples that are likely to improve the representations' transferability by measuring the variation, before and after annotation, of the transferability loss gradient.
Smooth And Consistent Probabilistic Regression Trees
We propose here a generalization of regression trees, referred to as Probabilistic Regression (PR) trees, that adapt to the smoothness of the prediction function relating input and output variables while preserving the interpretability of the prediction and being robust to noise.
Autoregressive Unsupervised Image Segmentation
In this work, we propose a new unsupervised image segmentation approach based on mutual information maximization between different constructed views of the inputs.
Ranked #5 on
Unsupervised Semantic Segmentation
on COCO-Stuff-3
Target Consistency for Domain Adaptation: when Robustness meets Transferability
Learning Invariant Representations has been successfully applied for reconciling a source and a target domain for Unsupervised Domain Adaptation.
Robust Domain Adaptation: Representations, Weights and Inductive Bias
The emergence of Domain Invariant Representations (IR) has improved drastically the transferability of representations from a labelled source domain to a new and unlabelled target domain.
An Overview of Deep Semi-Supervised Learning
Deep neural networks demonstrated their ability to provide remarkable performances on a wide range of supervised learning tasks (e. g., image classification) when trained on extensive collections of labeled data (e. g., ImageNet).
Semi-Supervised Semantic Segmentation with Cross-Consistency Training
To leverage the unlabeled examples, we enforce a consistency between the main decoder predictions and those of the auxiliary decoders, taking as inputs different perturbed versions of the encoder's output, and consequently, improving the encoder's representations.
Domain-Invariant Representations: A Look on Compression and Weights
Second, we show that learning weighted representations plays a key role in relaxing the constraint of invariance and then preserving the risk of compression.
Uncertain Trees: Dealing with Uncertain Inputs in Regression Trees
Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees, have been successfully used for regression in many applications and research studies.
