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Topic Summary

UC terminates subscriptions with world’s largest scientific publisher in push for open access to publicly funded research, since “Knowledge should not be accessible only to those who can pay,” said Robert May, chair of UC’s faculty Academic Senate. Similarly, machine learning should not be accessible only to those who can pay. Specifically, modern machine learning is migrating to the era of complex models (e.g., deep neural networks), which emphasizes the data representation highly. This learning paradigm is known as representation learning. Specifically, via deep neural networks, learned representations often result in much better performance than can be obtained with hand-designed representations. It is noted that representation learning normally requires a plethora of well-annotated data. Giant companies have enough money to collect well-annotated data. Nonetheless, for startups or non-profit organizations, such data is barely acquirable due to the cost of labeling data or the intrinsic scarcity in the given domain. These practical issues motivate us to research and pay attention to weakly-supervised representation learning (WSRL), since WSRL does not require such a huge amount of annotated data. We define WSRL as the collection of representation learning problem settings and algorithms that share the same goals as supervised representation learning but can only access to less supervised information than supervised representation learning. In this workshop, we discuss both theoretical and applied aspects of WSRL.

Topics of Interest

WSRL workshop includes but not limited to the following topics:

• Theories and applications of incomplete supervision, e.g., semi-supervised representation learning, active representation learning and positive-unlabeled representation learning;
• Theories and applications of inexact supervision, e.g., multi-instance representation learning and complementary representation learning;
• Theories and applications of inaccurate supervision, e.g., crowdsourced representation learning and label-noise representation learning;
• Theories and applications of cross-domain supervision, e.g., zero-/one-/few-shot representation learning, transferable representation learning and multi-task representation leaning;
• Theories and applications of imperfect demonstration, e.g., inverse reinforcement representation learning and imitation representation learning with non-expert demonstrations;
• Broad applications of weakly-supervised representation learning, such as weakly-supervised object detection, weakly-supervised sequence modeling, weakly-supervised cross-media retrieval, and weakly-supervised medical image segmentation.

Topic Description

The focus of this workshop is five types of weak supervision: incomplete supervision, inexact supervision, inaccurate supervision, cross-domain supervision and imperfect demonstration. Specifically, incomplete supervision considers a subset of training data given with ground-truth labels while the other data remain unlabeled, such as semi-supervised representation learning and positive-unlabeled representation learning. Inexact supervision considers the situation where some supervision information is given but not as exacted as desired, i.e., only coarse-grained labels are available. For example, if we are considering to classify every pixel of an image, rather than the image itself, then ImageNet becomes a benchmark with inexact supervision. Besides, multi-instance representation learning belongs to inexact supervision, where we do not exactly know which instance in the bag corresponds to the given ground-truth label. Inaccurate supervision considers the situation where the supervision information is not always the ground-truth, such as label-noise representation learning.

Cross-domain supervision considers the situation where the supervision information is scarce or even non-existent in the current domain but can be possibly derived from other domains. Examples of cross-domain supervision appear in zero-/one-/few-shot representation learning, where external knowledge from other domains is usually used to overcome the problem of too few or even no supervision in the original domain. Imperfect demonstration considers the situation for inverse reinforcement representation learning and imitation representation learning, where the agent learns with imperfect or non-expert demonstrations. For example, AlphaGo learns a policy from a sequence of states and actions (expert demonstration). Even if an expert player wins a game, it is not guaranteed that every action in the sequence is optimal.

This workshop will discuss the fundamental theory of weakly-supervised representation learning. Although theories of weakly-supervised statistical learning already exist, extending these results for weakly-supervised representation learning is still a challenge. Besides, this workshop also discusses on broad applications of weakly-supervised representation learning, such as weakly-supervised object detection (computer vision), weakly-supervised sequence modeling (natural language processing), weakly-supervised cross-media retrieval (information retrieval), and weakly-supervised medical image segmentation (healthcare analysis).

Submission Guidelines

Workshop submissions and camera ready versions will be handled by email. Please E-mail your paper to bhanml@comp.hkbu.edu.hk with subject line ACML20-WSRL-{paper name}. Papers should be formatted according to the ACML20 formatting instructions for the Conference Track. The submissions with 2 pages will be considered for the poster, while the submissions with at least 4 pages will be considered for the oral presentation.

ACML20-WSRL is a non-archival venue and there will be no published proceedings. The papers will be posted on the workshop website. It will be possible to submit to other conferences and journals both in parallel to and after ACML20-WSRL. Besides, we also welcome submissions to ACML20-WSRL that are under review at other conferences and workshops. At least one author from each accepted paper must register for the workshop. Please see the ACML 2020 Website for information about registration.

List of Invited Speakers

Kun Zhang, Carnegie Mellon University (confirmed)

Aditya K. Menon, Google Research (confirmed)

Schedule and Zoom Recordings

The workshop will be combined with invited talks, accepted presentations, and informal discussions.

The workshop is totally free online. Check our workshop recordings

Important Dates

Submission Deadline: 05:00 PM (Pacific Time), Nov 5th, 2020 (1st Round)

Acceptance Notifications: Nov 10th, 2020

Organizers

Bo Han, Hong Kong Baptist University / RIKEN, Hong Kong SAR, China.

Tongliang Liu, The University of Sydney, Australia.

Mingming Gong, The University of Melbourne, Australia.

Quanming Yao, Tsinghua University / 4Paradigm Inc., China.

Gang Niu, RIKEN, Japan.

Ivor W. Tsang, University of Technology Sydney, Australia.

Masashi Sugiyama, RIKEN / University of Tokyo, Japan.

Sponsors

4Paradigm Inc.

Previous Workshops

ACML2019 WSL Workshop, Nagoya, Japan.

SDM2020 WSUL Workshop, Ohio, United States.