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Found 12 papers, 1 papers with code
Application of Zone Method based Physics-Informed Neural Networks in Reheating Furnaces
Foundation Industries (FIs) constitute glass, metals, cement, ceramics, bulk chemicals, paper, steel, etc.
Fuse and Attend: Generalized Embedding Learning for Art and Sketches
While deep Embedding Learning approaches have witnessed widespread success in multiple computer vision tasks, the state-of-the-art methods for representing natural images need not necessarily perform well on images from other domains, such as paintings, cartoons, and sketch.
Multispectral Satellite Data Classification using Soft Computing Approach
A satellite image is a remotely sensed image data, where each pixel represents a specific location on earth.
A Tale of Color Variants: Representation and Self-Supervised Learning in Fashion E-Commerce
Our framework could be trained with supervisory signals in the form of triplets, that are obtained manually.
Seeing Objects in dark with Continual Contrastive Learning
We propose a novel, contrastive learning method to align the latent representations of a pair of real and synthetic images, to make the detector robust to the different domains.
Semi-Supervised Metric Learning: A Deep Resurrection
In this paper, we address the problem of Semi-Supervised DML (SSDML) that tries to learn a metric using a few labeled examples, and abundantly available unlabeled examples.
Color Variants Identification in Fashion e-commerce via Contrastive Self-Supervised Representation Learning
Interestingly, we observed that color variants are essentially manifestations of color jitter based augmentations.
Buy Me That Look: An Approach for Recommending Similar Fashion Products
The novelty and strength of our method lies in its capability to recommend similar articles for all the fashion items worn by the model, in addition to the primary article corresponding to the query.
Attr2Style: A Transfer Learning Approach for Inferring Fashion Styles via Apparel Attributes
The trained model is fine-tuned to generate style-based captions for the target dataset.
Unsupervised Deep Metric Learning via Orthogonality based Probabilistic Loss
As obtaining class labels in all applications is not feasible, we propose an unsupervised approach that learns a metric without making use of class labels.
Affinity guided Geometric Semi-Supervised Metric Learning
In this paper, we revamp the forgotten classical Semi-Supervised Distance Metric Learning (SSDML) problem from a Riemannian geometric lens, to leverage stochastic optimization within a end-to-end deep framework.
A Probabilistic approach for Learning Embeddings without Supervision
This restricts their applicability for large datasets in new applications where obtaining labels require extensive manual efforts and domain knowledge.
