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09/05/2023
Building a Winning Team: Selecting Source Model Ensembles using a Submodular Transferability Estimation Approach
Estimating the transferability of publicly available pretrained models t...
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05/29/2023
Rethinking Counterfactual Data Augmentation Under Confounding
Counterfactual data augmentation has recently emerged as a method to mit...
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03/26/2023
Δ-Networks for Efficient Model Patching
Models pre-trained on large-scale datasets are often finetuned to suppor...
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03/24/2023
Learning Causal Attributions in Neural Networks: Beyond Direct Effects
There has been a growing interest in capturing and maintaining causal re...
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01/17/2023
Towards Estimating Transferability using Hard Subsets
As transfer learning techniques are increasingly used to transfer knowle...
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11/09/2022
On the Robustness of Explanations of Deep Neural Network Models: A Survey
Explainability has been widely stated as a cornerstone of the responsibl...
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11/08/2022
Estimating Treatment Effects using Neurosymbolic Program Synthesis
Estimating treatment effects from observational data is a central proble...
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10/22/2022
Counterfactual Generation Under Confounding
A machine learning model, under the influence of observed or unobserved ...
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10/21/2022
Distilling the Undistillable: Learning from a Nasty Teacher
The inadvertent stealing of private/sensitive information using Knowledg...
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10/10/2022
ARUBA: An Architecture-Agnostic Balanced Loss for Aerial Object Detection
Deep neural networks tend to reciprocate the bias of their training data...
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07/21/2022
Novel Class Discovery without Forgetting
Humans possess an innate ability to identify and differentiate instances...
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06/13/2022
INDIGO: Intrinsic Multimodality for Domain Generalization
For models to generalize under unseen domains (a.k.a domain generalizati...
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05/08/2022
On Conditioning the Input Noise for Controlled Image Generation with Diffusion Models
Conditional image generation has paved the way for several breakthroughs...
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04/25/2022
Proto2Proto: Can you recognize the car, the way I do?
Prototypical methods have recently gained a lot of attention due to thei...
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04/22/2022
Spacing Loss for Discovering Novel Categories
Novel Class Discovery (NCD) is a learning paradigm, where a machine lear...
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03/30/2022
Unseen Classes at a Later Time? No Problem
Recent progress towards learning from limited supervision has encouraged...
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03/28/2022
Energy-based Latent Aligner for Incremental Learning
Deep learning models tend to forget their earlier knowledge while increm...
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12/10/2021
On Causally Disentangled Representations
Representation learners that disentangle factors of variation have alrea...
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11/24/2021
Causal Regularization Using Domain Priors
Neural networks leverage both causal and correlation-based relationships...
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11/10/2021
Feature Generation for Long-tail Classification
The visual world naturally exhibits an imbalance in the number of object...
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10/30/2021
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning Approach
Current SOTA adversarially robust models are mostly based on adversarial...
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10/23/2021
Multi-Domain Incremental Learning for Semantic Segmentation
Recent efforts in multi-domain learning for semantic segmentation attemp...
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08/25/2021
Inducing Semantic Grouping of Latent Concepts for Explanations: An Ante-Hoc Approach
Self-explainable deep models are devised to represent the hidden concept...
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07/15/2021
Context-Conditional Adaptation for Recognizing Unseen Classes in Unseen Domains
Recent progress towards designing models that can generalize to unseen d...
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07/12/2021
Structured Latent Embeddings for Recognizing Unseen Classes in Unseen Domains
The need to address the scarcity of task-specific annotated data has res...
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07/11/2021
Learn from Anywhere: Rethinking Generalized Zero-Shot Learning with Limited Supervision
A common problem with most zero and few-shot learning approaches is they...
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05/04/2021
Canonical Saliency Maps: Decoding Deep Face Models
As Deep Neural Network models for face processing tasks approach human-l...
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04/26/2021
Instance-wise Causal Feature Selection for Model Interpretation
We formulate a causal extension to the recently introduced paradigm of i...
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04/19/2021
Improving Attribution Methods by Learning Submodular Functions
This work explores the novel idea of learning a submodular scoring funct...
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03/03/2021
Towards Open World Object Detection
Humans have a natural instinct to identify unknown object instances in t...
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12/28/2020
Enhanced Regularizers for Attributional Robustness
Deep neural networks are the default choice of learning models for compu...
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12/08/2020
Data Instance Prior for Transfer Learning in GANs
Recent advances in generative adversarial networks (GANs) have shown rem...
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12/07/2020
A Deeper Look at the Hessian Eigenspectrum of Deep Neural Networks and its Applications to Regularization
Loss landscape analysis is extremely useful for a deeper understanding o...
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11/30/2020
On Initial Pools for Deep Active Learning
Active Learning (AL) techniques aim to minimize the training data requir...
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10/24/2020
Beyond VQA: Generating Multi-word Answer and Rationale to Visual Questions
Visual Question Answering is a multi-modal task that aims to measure hig...
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10/01/2020
Meta-Consolidation for Continual Learning
The ability to continuously learn and adapt itself to new tasks, without...
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08/17/2020
Zero Shot Domain Generalization
Standard supervised learning setting assumes that training data and test...
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08/08/2020
Assisting Scene Graph Generation with Self-Supervision
Research in scene graph generation has quickly gained traction in the pa...
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07/16/2020
An Empirical Study on the Robustness of NAS based Architectures
Most existing methods for Neural Architecture Search (NAS) focus on achi...
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07/15/2020
Enhancing Generalized Zero-Shot Learning via Adversarial Visual-Semantic Interaction
The performance of generative zero-shot methods mainly depends on the qu...
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06/18/2020
Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: A Survey
In light of growing challenges in agriculture with ever growing food dem...
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06/14/2020
On Saliency Maps and Adversarial Robustness
A Very recent trend has emerged to couple the notion of interpretability...
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04/30/2020
Generative Adversarial Data Programming
The paucity of large curated hand-labeled training data forms a major bo...
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03/14/2020
VarMixup: Exploiting the Latent Space for Robust Training and Inference
The vulnerability of Deep Neural Networks (DNNs) to adversarial attacks ...
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01/16/2020
A Little Fog for a Large Turn
Small, carefully crafted perturbations called adversarial perturbations ...
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11/29/2019
On the Benefits of Attributional Robustness
Interpretability is an emerging area of research in trustworthy machine ...
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10/04/2019
Active Learning with Weak Supervision for Cost-Effective Panicle Detection in Cereal Crops
Panicle density of cereal crops such as wheat and sorghum is one of the ...
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08/07/2019
An Adaptive Supervision Framework for Active Learning in Object Detection
Active learning approaches in computer vision generally involve querying...
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08/02/2019
AdvGAN++ : Harnessing latent layers for adversary generation
Adversarial examples are fabricated examples, indistinguishable from the...
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07/28/2019
