most citedCSGAN: Cyclic-Synthesized Generative Adversarial Networks for Image-to-Image Transformation

12 citations · 14 across the 3 of their papers we have counts for

collaborators

8 papers

eess.IV2020

PCSGAN: Perceptual Cyclic-Synthesized Generative Adversarial Networks for Thermal and NIR to Visible Image Transformation

Kancharagunta Kishan Babu, Shiv Ram Dubey

In many real world scenarios, it is difficult to capture the images in the visible light spectrum (VIS) due to bad lighting conditions. However, the images can be captured in such…

cs.CV2019

PSNet: Parametric Sigmoid Norm Based CNN for Face Recognition

Yash Srivastava, Vaishnav Murali, Shiv Ram Dubey

The Convolutional Neural Networks (CNN) have become very popular recently due to its outstanding performance in various computer vision applications. It is also used over widely st…

cs.LG20192 cited

NASIB: Neural Architecture Search withIn Budget

Abhishek Singh, Anubhav Garg, Jinan Zhou +2

Neural Architecture Search (NAS) represents a class of methods to generate the optimal neural network architecture and typically iterate over candidate architectures till convergen…

cs.CV2019

Spontaneous Facial Micro-Expression Recognition using 3D Spatiotemporal Convolutional Neural Networks

Sai Prasanna Teja Reddy, Surya Teja Karri, Shiv Ram Dubey +1

Facial expression recognition in videos is an active area of research in computer vision. However, fake facial expressions are difficult to be recognized even by humans. On the oth…

cs.CV2019

HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification

Swalpa Kumar Roy, Gopal Krishna, Shiv Ram Dubey +1

Hyperspectral image (HSI) classification is widely used for the analysis of remotely sensed images. Hyperspectral imagery includes varying bands of images. Convolutional Neural Net…

cs.CV2019

Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification

S. H. Shabbeer Basha, Shiv Ram Dubey, Viswanath Pulabaigari +1

The Convolutional Neural Networks (CNNs), in domains like computer vision, mostly reduced the need for handcrafted features due to its ability to learn the problem-specific feature…