Monte Carlo DropBlock for Modelling Uncertainty in Object Detection

With the advancements made in deep learning, computer vision problems like object detection and segmentation have seen a great improvement in performance. However, in many real-world applications such as autonomous driving vehicles, the risk associated with incorrect predictions of objects is very high. Standard deep learning models for object detection such as YOLO models are often overconfident in their predictions and do not take into account the uncertainty in predictions on out-of-distribution data. In this work, we propose an efficient and effective approach to model uncertainty in object detection and segmentation tasks using Monte-Carlo DropBlock (MC-DropBlock) based inference. The proposed approach applies drop-block during training time and test time on the convolutional layer of the deep learning models such as YOLO. We show that this leads to a Bayesian convolutional neural network capable of capturing the epistemic uncertainty in the model. Additionally, we capture the aleatoric uncertainty using a Gaussian likelihood. We demonstrate the effectiveness of the proposed approach on modeling uncertainty in object detection and segmentation tasks using out-of-distribution experiments. Experimental results show that MC-DropBlock improves the generalization, calibration, and uncertainty modeling capabilities of YOLO models in object detection and segmentation.

ZmBART: An Unsupervised Cross-lingual Transfer Framework for Language Generation

Despite the recent advancement in NLP research, cross-lingual transfer for natural language generation is relatively understudied. In this work, we transfer supervision from high resource language (HRL) to multiple low-resource languages (LRLs) for natural language generation (NLG). We consider four NLG tasks (text summarization, question generation, news headline generation, and distractor generation) and three syntactically diverse languages, i.e., English, Hindi, and Japanese. We propose an unsupervised cross-lingual language generation framework (called ZmBART) that does not use any parallel or pseudo-parallel/back-translated data. In this framework, we further pre-train mBART sequence-to-sequence denoising auto-encoder model with an auxiliary task using monolingual data of three languages. The objective function of the auxiliary task is close to the target tasks which enriches the multi-lingual latent representation of mBART and provides good initialization for target tasks. Then, this model is fine-tuned with task-specific supervised English data and directly evaluated with low-resource languages in the Zero-shot setting. To overcome catastrophic forgetting and spurious correlation issues, we applied freezing model component and data argumentation approaches respectively. This simple modeling approach gave us promising results.We experimented with few-shot training (with 1000 supervised data points) which boosted the model performance further. We performed several ablations and cross-lingual transferability analyses to demonstrate the robustness of ZmBART.

Removing Class Imbalance using Polarity-GAN: An Uncertainty Sampling Approach

Class imbalance is a challenging issue in practical classification problems for deep learning models as well as for traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured data handled by deep learning models. In this work, we propose to use a Generative Adversarial Network (GAN) equipped with a generator network G, a discriminator network D and a classifier network C to remove the class-imbalance in visual data sets. The generator network is initialized with auto-encoder to make it stable. The discriminator D ensures that G adheres to class distribution of imbalanced class. In conventional methods, where Generator G competes with discriminator D in a min-max game, we propose to further add an additional classifier network to the original network. Now, the generator network tries to compete in a min-max game with Discriminator as well as the new classifier that we have introduced. An additional condition is enforced on generator network G to produce points in the convex hull of desired imbalanced class. Further the contention of adversarial game with classifier C, pushes conditional distribution learned by G towards the periphery of the respective class, compensating the problem of class imbalance. Experimental evidence shows that this initialization results in stable training of the network. We achieve state of the art performance on extreme visual classification task on the FashionMNIST, MNIST, SVHN, ExDark, MVTec Anomaly Detection dataset, Chest X-Ray dataset and others.