Retrospective Loss: Looking Back to Improve Training of Deep Neural Networks
Deep neural networks (DNNs) are powerful learning machines that have enabled breakthroughs in several domains. In this work, we …
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ShapeVis: High-dimensional Data Visualization at Scale
We present ShapeVis, a scalable visualization technique for point cloud data inspired from topological data analysis. Our method …
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SieveNet: A Unified Framework for Robust Image-based Virtual Try-On
Image-based virtual try-on for fashion has attracted considerable attention recently. The task requires trying on the desired clothing …
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SimPropNet: Improved Similarity Propagation for Few-shot Image Segmentation
Few-shot segmentation (FSS) methods perform image segmentation for a particular object class in a target (query) image, using a small …
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Towards A Unified Framework for Visual Compatibility Prediction
Visual compatibility prediction refers to the task of determining if a set of items go well together. Existing techniques for …
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Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models
Neural networks are vulnerable to adversarial attacks – small visually imperceptible crafted noise which when added to the input …
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Hush-Hush Speak: Speech Reconstruction Using Silent Videos
Speech Reconstruction is the task of recreation of speech using silent videos as input. In the literature, it is also referred to as …
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Lipper: Synthesizing Thy Speech using Multi-View Lipreading
Lipreading has a lot of potential applications such as in the domain of surveillance and video conferencing. Despite this, most of the …
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OpticalGAN : Generative Adversarial Networks for Continuous Variable Quantum Computation
We present OpticalGAN, an extension of quantum generative adversarial networks for continuous-variable quantum computation. OpticalGAN …
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Powering Robust Fashion Retrieval with Information Rich Feature Embeddings
Visual content based product retrieval has become increasingly important for e-commerce. Fashion retrieval, in particular, is a …
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