Search Results for author:
Found 20 papers, 10 papers with code
LoMOE: Localized Multi-Object Editing via Multi-Diffusion
Our experiments against existing state-of-the-art methods demonstrate the improved effectiveness of our approach in terms of both image editing quality and inference speed.
FAST: Feature Aware Similarity Thresholding for Weak Unlearning in Black-Box Generative Models
The heightened emphasis on the regulation of deep generative models, propelled by escalating concerns pertaining to privacy and compliance with regulatory frameworks, underscores the imperative need for precise control mechanisms over these models.
Fusing Conditional Submodular GAN and Programmatic Weak Supervision
In our work, we address these challenges by (i) implementing a noise-aware classifier using the pseudo labels generated by the label model (ii) utilizing the noise-aware classifier's prediction to train the label model and generate class-conditional images.
DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting
While there exist multiple, general-purpose, deep learning-based object detection and counting methods, they may not readily transfer to detecting and counting cells in medical images, due to the limited data, presence of tiny overlapping objects, multiple cell types, severe class-imbalance, minute differences in size/shape of cells, etc.
Discovering mesoscopic descriptions of collective movement with neural stochastic modelling
Collective motion is an ubiquitous phenomenon in nature, inspiring engineers, physicists and mathematicians to develop mathematical models and bio-inspired designs.
Adaptive Mixing of Auxiliary Losses in Supervised Learning
In several supervised learning scenarios, auxiliary losses are used in order to introduce additional information or constraints into the supervised learning objective.
ScRAE: Deterministic Regularized Autoencoders with Flexible Priors for Clustering Single-cell Gene Expression Data
The basic idea in RAEs is to learn a non-linear mapping from the high-dimensional data space to a low-dimensional latent space and vice-versa, simultaneously imposing a distributional prior on the latent space, which brings in a regularization effect.
Generalization on Unseen Domains via Inference-Time Label-Preserving Target Projections
Generalization of machine learning models trained on a set of source domains on unseen target domains with different statistics, is a challenging problem.
Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation
We evaluate our method on four public medical segmentation datasets and a novel histopathology dataset that we introduce.
Domain Generalization via Inference-time Label-Preserving Target Projections
Generalization of machine learning models trained on a set of source domains on unseen target domains with different statistics, is a challenging problem.
A Variational Information Bottleneck Based Method to Compress Sequential Networks for Human Action Recognition
In the last few years, compression of deep neural networks has become an important strand of machine learning and computer vision research.
Discrepancy Minimization in Domain Generalization with Generative Nearest Neighbors
Features extracted from this source domain are learned using a generative model whose latent space is used as a sampler to retrieve the nearest neighbors for the target data points.
Unsupervised Domain Adaptation for Semantic Segmentation of NIR Images through Generative Latent Search
Therefore we cast the skin segmentation problem as that of target-independent Unsupervised Domain Adaptation (UDA) where we use the data from the Red-channel of the visible-range to develop skin segmentation algorithm on NIR images.
To Regularize or Not To Regularize? The Bias Variance Trade-off in Regularized AEs
Specifically, we consider the class of RAEs with deterministic Encoder-Decoder pairs, Wasserstein Auto-Encoders (WAE), and show that having a fixed prior distribution, \textit{a priori}, oblivious to the dimensionality of the `true' latent space, will lead to the infeasibility of the optimization problem considered.
C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation
Estimation of information theoretic quantities such as mutual information and its conditional variant has drawn interest in recent times owing to their multifaceted applications.
Target-Independent Domain Adaptation for WBC Classification using Generative Latent Search
We prove the existence of such a clone given that infinite number of data points can be sampled from the source distribution.
MaskAAE: Latent space optimization for Adversarial Auto-Encoders
The field of neural generative models is dominated by the highly successful Generative Adversarial Networks (GANs) despite their challenges, such as training instability and mode collapse.
Guided Weak Supervision for Action Recognition with Scarce Data to Assess Skills of Children with Autism
In this paper, we propose to automate the response measurement through video recording of the scene following the use of Deep Neural models for human action recognition from videos.
Variational Inference with Latent Space Quantization for Adversarial Resilience
Specifically, we simultaneously auto-encode the data manifold and its perturbations implicitly through the perturbations of the regularized and quantized generative latent space, realized using variational inference.
Unsupervised Conditional Generation using noise engineered mode matching GAN
Conditional generation refers to the process of sampling from an unknown distribution conditioned on semantics of the data.
