3.3k citations · 4.2k across the 69 of their papers we have counts for
14 papers · 1 filter
Attributional Robustness Training using Input-Gradient Spatial Alignment
Mayank Singh, Nupur Kumari, Puneet Mangla +3
Interpretability is an emerging area of research in trustworthy machine learning. Safe deployment of machine learning system mandates that the prediction and its explanation be rel…
Active Learning with Point Supervision for Cost-Effective Panicle Detection in Cereal Crops
Akshay L Chandra, Sai Vikas Desai, Vineeth N Balasubramanian +2
Panicle density of cereal crops such as wheat and sorghum is one of the main components for plant breeders and agronomists in understanding the yield of their crops. To phenotype t…
An Adaptive Supervision Framework for Active Learning in Object Detection
Sai Vikas Desai, Akshay L Chandra, Wei Guo +2
Active learning approaches in computer vision generally involve querying strong labels for data. However, previous works have shown that weak supervision can be effective in traini…
AdvGAN++ : Harnessing latent layers for adversary generation
Puneet Mangla, Surgan Jandial, Sakshi Varshney +1
Adversarial examples are fabricated examples, indistinguishable from the original image that mislead neural networks and drastically lower their performance. Recently proposed AdvG…
Charting the Right Manifold: Manifold Mixup for Few-shot Learning
Puneet Mangla, Mayank Singh, Abhishek Sinha +3
Few-shot learning algorithms aim to learn model parameters capable of adapting to unseen classes with the help of only a few labeled examples. A recent regularization technique - M…
Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models
Mayank Singh, Abhishek Sinha, Nupur Kumari +3
Neural networks are vulnerable to adversarial attacks -- small visually imperceptible crafted noise which when added to the input drastically changes the output. The most effective…
Submodular Batch Selection for Training Deep Neural Networks
K J Joseph, Vamshi Teja R, Krishnakant Singh +1
Mini-batch gradient descent based methods are the de facto algorithms for training neural network architectures today. We introduce a mini-batch selection strategy based on submodu…
Automatic estimation of heading date of paddy rice using deep learning
Sai Vikas Desai, Vineeth N Balasubramanian, Tokihiro Fukatsu +2
Accurate estimation of heading date of paddy rice greatly helps the breeders to understand the adaptability of different crop varieties in a given location. The heading date also p…
Borrow from Anywhere: Pseudo Multi-modal Object Detection in Thermal Imagery
Chaitanya Devaguptapu, Ninad Akolekar, Manuj M Sharma +1
Can we improve detection in the thermal domain by borrowing features from rich domains like visual RGB? In this paper, we propose a pseudo-multimodal object detector trained on nat…
Teaching GANs to Sketch in Vector Format
Varshaneya V, S Balasubramanian, Vineeth N Balasubramanian
Sketching is more fundamental to human cognition than speech. Deep Neural Networks (DNNs) have achieved the state-of-the-art in speech-related tasks but have not made significant d…
Zero-Shot Task Transfer
Arghya Pal, Vineeth N Balasubramanian
In this work, we present a novel meta-learning algorithm, i.e. TTNet, that regresses model parameters for novel tasks for which no ground truth is available (zero-shot tasks). In o…
Neural Network Attributions: A Causal Perspective
Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar +1
We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such). The neural network architectur…
DANTE: Deep AlterNations for Training nEural networks
Vaibhav B Sinha, Sneha Kudugunta, Adepu Ravi Sankar +3
We present DANTE, a novel method for training neural networks using the alternating minimization principle. DANTE provides an alternate perspective to traditional gradient-based ba…
Low-Cost Transfer Learning of Face Tasks
Thrupthi Ann John, Isha Dua, Vineeth N Balasubramanian +1
Do we know what the different filters of a face network represent? Can we use this filter information to train other tasks without transfer learning? For instance, can age, head po…