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Found 29 papers, 8 papers with code
Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification
Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce.
Understanding the Local Geometry of Generative Model Manifolds
Deep generative models learn continuous representations of complex data manifolds using a finite number of samples during training.
Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities
The rise of foundation models holds immense promise for advancing AI, but this progress may amplify existing risks and inequalities, leaving marginalized communities behind.
DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations
Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated with the target class, leading to poor generalization and biased predictions.
Source-free Domain Adaptation Requires Penalized Diversity
In addition, PD is augmented with a weighted MI maximization objective for label distribution shift.
Pitfalls of Conditional Batch Normalization for Contextual Multi-Modal Learning
Despite the boost in performance observed by using CBN layers, our work reveals that the visual features learned by introducing auxiliary data via CBN deteriorates.
FL Games: A Federated Learning Framework for Distribution Shifts
Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server.
FHIST: A Benchmark for Few-shot Classification of Histological Images
We build few-shot tasks and base-training data with various tissue types, different levels of domain shifts stemming from various cancer sites, and different class-granularity levels, thereby reflecting realistic scenarios.
FL Games: A federated learning framework for distribution shifts
Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server.
AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation
In Federated Learning (FL), a number of clients or devices collaborate to train a model without sharing their data.
Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning
Batch Normalization is a staple of computer vision models, including those employed in few-shot learning.
CT-SGAN: Computed Tomography Synthesis GAN
Diversity in data is critical for the successful training of deep learning models.
Hypothesis Disparity Regularized Mutual Information Maximization
We propose a hypothesis disparity regularized mutual information maximization~(HDMI) approach to tackle unsupervised hypothesis transfer -- as an effort towards unifying hypothesis transfer learning (HTL) and unsupervised domain adaptation (UDA) -- where the knowledge from a source domain is transferred solely through hypotheses and adapted to the target domain in an unsupervised manner.
Conditional Generation of Medical Images via Disentangled Adversarial Inference
Current practices in using cGANs for medical image generation, only use a single variable for image generation (i. e., content) and therefore, do not provide much flexibility nor control over the generated image.
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
We present an approach for unsupervised domain adaptation---with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution shift---from a class-conditioned domain alignment perspective.
Ranked #1 on
Unsupervised Domain Adaptation
on Office-Home
(Avg accuracy metric)
Jigsaw-VAE: Towards Balancing Features in Variational Autoencoders
The latent variables learned by VAEs have seen considerable interest as an unsupervised way of extracting features, which can then be used for downstream tasks.
DFNet: Discriminative feature extraction and integration network for salient object detection
To address the second challenge, we propose an Attention-based Multi-level Integrator Module to give the model the ability to assign different weights to multi-level feature maps.
FoCL: Feature-Oriented Continual Learning for Generative Models
In this paper, we propose a general framework in continual learning for generative models: Feature-oriented Continual Learning (FoCL).
Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay
Learning in non-stationary environments is one of the biggest challenges in machine learning.
CAGNet: Content-Aware Guidance for Salient Object Detection
Beneficial from Fully Convolutional Neural Networks (FCNs), saliency detection methods have achieved promising results.
Dual Adversarial Inference for Text-to-Image Synthesis
), and the style, which is usually not well described in the text (e. g., location, quantity, size, etc.).
Learning to Learn with Conditional Class Dependencies
Neural networks can learn to extract statistical properties from data, but they seldom make use of structured information from the label space to help representation learning.
InfoMask: Masked Variational Latent Representation to Localize Chest Disease
The scarcity of richly annotated medical images is limiting supervised deep learning based solutions to medical image analysis tasks, such as localizing discriminatory radiomic disease signatures.
On the Importance of Attention in Meta-Learning for Few-Shot Text Classification
Based on the Model-Agnostic Meta-Learning framework (MAML), we introduce the Attentive Task-Agnostic Meta-Learning (ATAML) algorithm for text classification.
Learnable Explicit Density for Continuous Latent Space and Variational Inference
In this paper, we study two aspects of the variational autoencoder (VAE): the prior distribution over the latent variables and its corresponding posterior.
Deep learning trends for focal brain pathology segmentation in MRI
In this chapter, we provide a survey of CNN methods applied to medical imaging with a focus on brain pathology segmentation.
HeMIS: Hetero-Modal Image Segmentation
We introduce a deep learning image segmentation framework that is extremely robust to missing imaging modalities.
Ranked #104 on
Semantic Segmentation
on NYU Depth v2
Within-Brain Classification for Brain Tumor Segmentation
Purpose: In this paper, we investigate a framework for interactive brain tumor segmentation which, at its core, treats the problem of interactive brain tumor segmentation as a machine learning problem.
Brain Tumor Segmentation with Deep Neural Networks
Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN.
Ranked #1 on
Brain Tumor Segmentation
on BRATS-2013 leaderboard
