Sachin Farfade

Vu Nguyen, Hisham Husain, Sachin Farfade et al.

Semi-supervised learning is a critical tool in reducing machine learning's dependence on labeled data. It has been successfully applied to structured data, such as images and natural language, by exploiting the inherent spatial and semantic structure therein with pretrained models or data augmentation. These methods are not applicable, however, when the data does not have the appropriate structure, or invariances. Due to their simplicity, pseudo-labeling (PL) methods can be widely used without any domain assumptions. However, the greedy mechanism in PL is sensitive to a threshold and can perform poorly if wrong assignments are made due to overconfidence. This paper studies theoretically the role of uncertainty to pseudo-labeling and proposes Confident Sinkhorn Allocation (CSA), which identifies the best pseudo-label allocation via optimal transport to only samples with high confidence scores. CSA outperforms the current state-of-the-art in this practically important area of semi-supervised learning. Additionally, we propose to use the Integral Probability Metrics to extend and improve the existing PACBayes bound which relies on the Kullback-Leibler (KL) divergence, for ensemble models. Our code is publicly available at https://github.com/amzn/confident-sinkhorn-allocation.

Lu Jiang, Junwei Liang, Liangliang Cao et al.

This paper proposes a new task, MemexQA: given a collection of photos or videos from a user, the goal is to automatically answer questions that help users recover their memory about events captured in the collection. Towards solving the task, we 1) present the MemexQA dataset, a large, realistic multimodal dataset consisting of real personal photos and crowd-sourced questions/answers, 2) propose MemexNet, a unified, end-to-end trainable network architecture for image, text and video question answering. Experimental results on the MemexQA dataset demonstrate that MemexNet outperforms strong baselines and yields the state-of-the-art on this novel and challenging task. The promising results on TextQA and VideoQA suggest MemexNet's efficacy and scalability across various QA tasks.

Kofi Boakye, Sachin Farfade, Hamid Izadinia et al.

Automated photo tagging has established itself as one of the most compelling applications of deep learning. While deep convolutional neural networks have repeatedly demonstrated top performance on standard datasets for classification, there are a number of often overlooked but important considerations when deploying this technology in a real-world scenario. In this paper, we present our efforts in developing a large-scale photo tagging system for Flickr photo search. We discuss topics including how to 1) select the tags that matter most to our users; 2) develop lightweight, high-performance models for tag prediction; and 3) leverage the power of large amounts of noisy data for training. Our results demonstrate that, for real-world datasets, training exclusively with this noisy data yields performance on par with the standard paradigm of first pre-training on clean data and then fine-tuning. In addition, we observe that the models trained with user-generated data can yield better fine-tuning results when a small amount of clean data is available. As such, we advocate for the approach of harnessing user-generated data in large-scale systems.