145 research outputs found
Model Sparsification Can Simplify Machine Unlearning
Recent data regulations necessitate machine unlearning (MU): The removal of
the effect of specific examples from the model. While exact unlearning is
possible by conducting a model retraining with the remaining data from scratch,
its computational cost has led to the development of approximate but efficient
unlearning schemes. Beyond data-centric MU solutions, we advance MU through a
novel model-based viewpoint: sparsification via weight pruning. Our results in
both theory and practice indicate that model sparsity can boost the
multi-criteria unlearning performance of an approximate unlearner, closing the
approximation gap, while continuing to be efficient. With this insight, we
develop two new sparsity-aware unlearning meta-schemes, termed `prune first,
then unlearn' and `sparsity-aware unlearning'. Extensive experiments show that
our findings and proposals consistently benefit MU in various scenarios,
including class-wise data scrubbing, random data scrubbing, and backdoor data
forgetting. One highlight is the 77% unlearning efficacy gain of fine-tuning
(one of the simplest approximate unlearning methods) in the proposed
sparsity-aware unlearning paradigm. Codes are available at
https://github.com/OPTML-Group/Unlearn-Sparse
The SpeakIn System Description for CNSRC2022
This report describes our speaker verification systems for the tasks of the
CN-Celeb Speaker Recognition Challenge 2022 (CNSRC 2022). This challenge
includes two tasks, namely speaker verification(SV) and speaker retrieval(SR).
The SV task involves two tracks: fixed track and open track. In the fixed
track, we only used CN-Celeb.T as the training set. For the open track of the
SV task and SR task, we added our open-source audio data. The ResNet-based,
RepVGG-based, and TDNN-based architectures were developed for this challenge.
Global statistic pooling structure and MQMHA pooling structure were used to
aggregate the frame-level features across time to obtain utterance-level
representation. We adopted AM-Softmax and AAM-Softmax combined with the
Sub-Center method to classify the resulting embeddings. We also used the
Large-Margin Fine-Tuning strategy to further improve the model performance. In
the backend, Sub-Mean and AS-Norm were used. In the SV task fixed track, our
system was a fusion of five models, and two models were fused in the SV task
open track. And we used a single system in the SR task. Our approach leads to
superior performance and comes the 1st place in the open track of the SV task,
the 2nd place in the fixed track of the SV task, and the 3rd place in the SR
task.Comment: 4 page
To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now
The recent advances in diffusion models (DMs) have revolutionized the
generation of complex and diverse images. However, these models also introduce
potential safety hazards, such as the production of harmful content and
infringement of data copyrights. Although there have been efforts to create
safety-driven unlearning methods to counteract these challenges, doubts remain
about their capabilities. To bridge this uncertainty, we propose an evaluation
framework built upon adversarial attacks (also referred to as adversarial
prompts), in order to discern the trustworthiness of these safety-driven
unlearned DMs. Specifically, our research explores the (worst-case) robustness
of unlearned DMs in eradicating unwanted concepts, styles, and objects,
assessed by the generation of adversarial prompts. We develop a novel
adversarial learning approach called UnlearnDiff that leverages the inherent
classification capabilities of DMs to streamline the generation of adversarial
prompts, making it as simple for DMs as it is for image classification attacks.
This technique streamlines the creation of adversarial prompts, making the
process as intuitive for generative modeling as it is for image classification
assaults. Through comprehensive benchmarking, we assess the unlearning
robustness of five prevalent unlearned DMs across multiple tasks. Our results
underscore the effectiveness and efficiency of UnlearnDiff when compared to
state-of-the-art adversarial prompting methods. Codes are available at
https://github.com/OPTML-Group/Diffusion-MU-Attack. WARNING: This paper
contains model outputs that may be offensive in nature.Comment: Codes are available at
https://github.com/OPTML-Group/Diffusion-MU-Attac
Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning
Massive data is often considered essential for deep learning applications,
but it also incurs significant computational and infrastructural costs.
Therefore, dataset pruning (DP) has emerged as an effective way to improve data
efficiency by identifying and removing redundant training samples without
sacrificing performance. In this work, we aim to address the problem of DP for
transfer learning, i.e., how to prune a source dataset for improved pretraining
efficiency and lossless finetuning accuracy on downstream target tasks. To our
best knowledge, the problem of DP for transfer learning remains open, as
previous studies have primarily addressed DP and transfer learning as separate
problems. By contrast, we establish a unified viewpoint to integrate DP with
transfer learning and find that existing DP methods are not suitable for the
transfer learning paradigm. We then propose two new DP methods, label mapping
and feature mapping, for supervised and self-supervised pretraining settings
respectively, by revisiting the DP problem through the lens of source-target
domain mapping. Furthermore, we demonstrate the effectiveness of our approach
on numerous transfer learning tasks. We show that source data classes can be
pruned by up to 40% ~ 80% without sacrificing downstream performance, resulting
in a significant 2 ~ 5 times speed-up during the pretraining stage. Besides,
our proposal exhibits broad applicability and can improve other computationally
intensive transfer learning techniques, such as adversarial pretraining. Codes
are available at https://github.com/OPTML-Group/DP4TL.Comment: Thirty-seventh Conference on Neural Information Processing Systems
(NeurIPS 2023
You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray
Chest X-ray (CXR) anatomical abnormality detection aims at localizing and
characterising cardiopulmonary radiological findings in the radiographs, which
can expedite clinical workflow and reduce observational oversights. Most
existing methods attempted this task in either fully supervised settings which
demanded costly mass per-abnormality annotations, or weakly supervised settings
which still lagged badly behind fully supervised methods in performance. In
this work, we propose a co-evolutionary image and report distillation (CEIRD)
framework, which approaches semi-supervised abnormality detection in CXR by
grounding the visual detection results with text-classified abnormalities from
paired radiology reports, and vice versa. Concretely, based on the classical
teacher-student pseudo label distillation (TSD) paradigm, we additionally
introduce an auxiliary report classification model, whose prediction is used
for report-guided pseudo detection label refinement (RPDLR) in the primary
vision detection task. Inversely, we also use the prediction of the vision
detection model for abnormality-guided pseudo classification label refinement
(APCLR) in the auxiliary report classification task, and propose a co-evolution
strategy where the vision and report models mutually promote each other with
RPDLR and APCLR performed alternatively. To this end, we effectively
incorporate the weak supervision by reports into the semi-supervised TSD
pipeline. Besides the cross-modal pseudo label refinement, we further propose
an intra-image-modal self-adaptive non-maximum suppression, where the pseudo
detection labels generated by the teacher vision model are dynamically
rectified by high-confidence predictions by the student. Experimental results
on the public MIMIC-CXR benchmark demonstrate CEIRD's superior performance to
several up-to-date weakly and semi-supervised methods
SYK-targeted dendritic cell-mediated cytotoxic T lymphocytes enhance the effect of immunotherapy on retinoblastoma
Purpose: Retinoblastoma (RB) is the most common primary intraocular tumor in children. Chemotherapy is currently the main method of RB treatment. Unfortunately, RB often becomes chemoresistant and turns lethal. Here, we used in vitro cell immunotherapy to explore whether adoptive immunotherapy could be used as a potential treatment for RB. We focused on spleen tyrosine kinase (SYK), which is significantly upregulated in RB cells and serves as a marker for RB cells. Methods: Using lentiviruses, we genetically modified dendritic cells (DCs) to express and present the SYK peptide antigen to cytotoxic T lymphocytes (CTLs) in vitro. We used SYK-negative cell lines (MDA-MB-231, MCF-10A, and hTERT-RPE1) and SYK-positive cell lines (MCF-7 and RB-Y79) to evaluate the specificity and cytotoxicity of DC presented CTLs using FACS, live-cell imaging, and RNA interference. Results: The cytotoxicity of CTLs induced by SYK-overexpressing DCs (SYK-DC–CTLs) was enhanced more than three times in SYK-positive cell lines compared with SYK-negative cell lines. DCs primed with SYK could drive CTL cytotoxicity against SYK-positive cell lines but not against SYK-negative cell lines. Moreover, SYK-silenced RB-Y79 cells successfully evaded the cytotoxic attack from SYK-DC–CTLs. However, SYK-DC–CTLs could target SYK overexpressed hTERT-RPE1 cells, suggesting that SYK is a specific antigen for RB. Furthermore, SYK-DC–CTL exhibited specific cytotoxicity against carboplatin-resistant RB-Y79 cells in vitro. Conclusions: Our data showed that SYK could be a potential immunotherapy target mediated by DCs. We propose SYK as a candidate target for treatment of chemoresistant RB.Fil: Chen, Xuemei. Xi'an Jiaotong University; ChinaFil: Kunda, Patricia Elena. Instituto Universitario de Ciencias BiomĂ©dicas de CĂłrdoba; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - CĂłrdoba; ArgentinaFil: Lin, Jianwei. Shenzhen University; ChinaFil: Zhou, Meiling. Shenzhen Luohu Peoples Hospital; China. Shenzhen University; ChinaFil: Huang, Jinghan. Shenzhen Luohu Peoples Hospital; ChinaFil: Zhang, Huqin. Xi'an Jiaotong University; ChinaFil: Liu, Tao. Shenzhen University; China. Shenzhen Luohu Peoples Hospital; Chin
FELM: Benchmarking Factuality Evaluation of Large Language Models
Assessing factuality of text generated by large language models (LLMs) is an
emerging yet crucial research area, aimed at alerting users to potential errors
and guiding the development of more reliable LLMs. Nonetheless, the evaluators
assessing factuality necessitate suitable evaluation themselves to gauge
progress and foster advancements. This direction remains under-explored,
resulting in substantial impediments to the progress of factuality evaluators.
To mitigate this issue, we introduce a benchmark for Factuality Evaluation of
large Language Models, referred to as felm. In this benchmark, we collect
responses generated from LLMs and annotate factuality labels in a fine-grained
manner. Contrary to previous studies that primarily concentrate on the
factuality of world knowledge (e.g.~information from Wikipedia), felm focuses
on factuality across diverse domains, spanning from world knowledge to math and
reasoning. Our annotation is based on text segments, which can help pinpoint
specific factual errors. The factuality annotations are further supplemented by
predefined error types and reference links that either support or contradict
the statement. In our experiments, we investigate the performance of several
LLM-based factuality evaluators on felm, including both vanilla LLMs and those
augmented with retrieval mechanisms and chain-of-thought processes. Our
findings reveal that while retrieval aids factuality evaluation, current LLMs
are far from satisfactory to faithfully detect factual errors.Comment: Accepted by NeurIPS 2023 Track on Datasets and Benchmark
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