16 citations · 21 across the 4 of their papers we have counts for
4 papers
Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives
Shrinivas Ramasubramanian, Harsh Rangwani, Sho Takemori +3
The rise in internet usage has led to the generation of massive amounts of data, resulting in the adoption of various supervised and semi-supervised machine learning algorithms, wh…
Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics
Harsh Rangwani, Shrinivas Ramasubramanian, Sho Takemori +3
Self-training based semi-supervised learning algorithms have enabled the learning of highly accurate deep neural networks, using only a fraction of labeled data. However, the major…
Self-Gated Memory Recurrent Network for Efficient Scalable HDR Deghosting
K. Ram Prabhakar, Susmit Agrawal, R. Venkatesh Babu
We propose a novel recurrent network-based HDR deghosting method for fusing arbitrary length dynamic sequences. The proposed method uses convolutional and recurrent architectures t…
SwiDeN : Convolutional Neural Networks For Depiction Invariant Object Recognition
Ravi Kiran Sarvadevabhatla, Shiv Surya, Srinivas S S Kruthiventi +1
Current state of the art object recognition architectures achieve impressive performance but are typically specialized for a single depictive style (e.g. photos only, sketches only…