In this paper, we focus on multi-task classification, where related classification tasks share the same label space and are learned simultaneously. In particular, we tackle a new setting, which is more realistic than currently addressed in the literature, where categories shift from training to test data. Hence, individual tasks do not contain complete training data for the categories in the test set. To generalize to such test data, it is crucial for individual tasks to leverage knowledge from related tasks. To this end, we propose learning an association graph to transfer knowledge among tasks for missing classes.

In this paper, we focus on multi-task classification, where related classification tasks share the same label space and are learned simultaneously.

In this paper, we focus on multi-task classification, where related classification tasks share the same label space and are learned simultaneously. In particular, we tackle a new setting, which is more realistic than currently addressed in the literature, where categories shift from training to test data. Hence, individual tasks do not contain complete training data for the categories in the test set. To generalize to such test data, it is crucial for individual tasks to leverage knowledge from related tasks. To this end, we propose learning an association graph to transfer knowledge among tasks for missing classes.

A domain (distribution) shift between training and test data often hinders the real-world performance of deep neural networks, necessitating unsupervised domain adaptation (UDA) to bridge this gap. Online source-free UDA has emerged as a solution for practical scenarios where access to source data is restricted and target data is received as a continuous stream. However, the open-world nature of many real-world applications additionally introduces category shifts meaning that the source and target label spaces may differ. Online source-free universal domain adaptation (SF-UniDA) addresses this challenge. Existing methods mainly rely on self-training with pseudo-labels, yet the relationship between pseudo-labeling and adaptation outcomes has not been studied yet. To bridge this gap, we conduct a systematic analysis through controlled experiments with simulated pseudo-labeling, offering valuable insights into pseudo-labeling for online SF-UniDA. Our findings reveal a substantial gap between the current state-of-the-art and the upper bound of adaptation achieved with perfect pseudo-labeling. Moreover, we show that a contrastive loss enables effective adaptation even with moderate pseudo-label accuracy, while a cross-entropy (CE) loss, though less robust to pseudo-label errors, achieves superior results when pseudo-labeling approaches perfection. Lastly, our findings indicate that pseudo-label accuracy is in general more crucial than quantity, suggesting that prioritizing fewer but high-confidence pseudo-labels is beneficial. Overall, our study highlights the critical role of pseudo-labeling in (online) SF-UniDA and provides actionable insights to drive future advancements in the field. Our code is available at https://github.com/pascalschlachter/PLAnalysis.

In this paper, we focus on multi-task classification, where related classification tasks share the same label space and are learned simultaneously. In particular, we tackle a new setting, which is more realistic than currently addressed in the literature, where categories shift from training to test data. Hence, individual tasks do not contain complete training data for the categories in the test set. To generalize to such test data, it is crucial for individual tasks to leverage knowledge from related tasks. To this end, we propose learning an association graph to transfer knowledge among tasks for missing classes.

ORC ID: 0000-0003-2376-4591 Abstract Text continues to remain a relevant form of representation for information. Text documents are created either in digital native platforms or through conversion of other media files such as images and speech. While the digital native text is invariably obtained through physical or virtual keyboards, technologies such as OCR & speech recognition are utilized to transform the images and speech signals to text content. All these variety of mechanisms of text generation also introduce error into the captured text. This project aims at analyzing different kinds of errors that occurs in text documents. The work employs two of the advanced deep neural network based language models, namely, BART and MarianMT, for rectifying the anomalies present in text. Transfer learning of these models with available dataset is performed to finetune their capacity for error correction. A comparative study is conducted to investigate the effectiveness of these models in handling each of the defined error categories. It is observed that while both the models are able to bring down the erroneous sentences by 20+%, BART is able to handle spelling errors far better (24.6%) than grammatical errors (8.8%). I. Introduction Text is a natural representation of all the existing languages in the world. Texts help one express and communicate with others. Handwritten texts have been part of the history for ages, while digital texts have evolved to keep up with the rapidly growing technology in day to day lives. It is due to texts that one can extend from their knowledge and memory beyond their body into the environment around [1]. Text is available in various forms, from handwritten manuscripts to This is a pre-print version of the paper. Texts can be utilized for personal reasons such as diary entry, blog, etc., as well as for professional purposes like advertising, surveying, etc. Right from the newspaper one reads in the morning to the social media scrolling before going to bed, people are surrounded by text. It is human nature to categorize any kind of data they receive. As there is so much text available around, it is obvious that humans tend to inspect and review the text they require. It is the process of scanning the textual data in order to derive some meaning and store information. Most businesses rely on text analysis to extract valuable insights from various raw sources. The feedback received from these sources such as emails, chat messages, social media posts, comments & statements and survey responses help them in their decision-making strategies.

Source-Free Domain Adaptation (SFDA) presents a promising solution to this dilemma, yet most SFDA approaches are restricted to closed-set scenarios. In this paper, we explore Source-Free Universal Domain Adaptation (SF-UniDA) aiming to accurately classify "known" data belonging to common categories and segregate them from target-private "unknown" data. We propose a novel Global and Local Clustering (GLC) technique, which comprises an adaptive one-vs-all global clustering algorithm to discern between target classes, complemented by a local k-NN clustering strategy to mitigate negative transfer. Despite the effectiveness, the inherent closed-set source architecture leads to uniform treatment of "unknown" data, impeding the identification of distinct "unknown" categories. To address this, we evolve GLC to GLC++, integrating a contrastive affinity learning strategy. We examine the superiority of GLC and GLC++ across multiple benchmarks and category shift scenarios. Remarkably, in the most challenging open-partial-set scenarios, GLC and GLC++ surpass GATE by 16.7% and 18.6% in H-score on VisDA, respectively. GLC++ enhances the novel category clustering accuracy of GLC by 4.3% in open-set scenarios on Office-Home. Furthermore, the introduced contrastive learning strategy not only enhances GLC but also significantly facilitates existing methodologies.

Vision-language models (VLMs) [Radford et al., 2021, Li et al., 2022a, Jia et al., 2021, Li et al., 2022c] pre-trained on web-scale image-text pairs have exhibited robust zero-shot prediction capabilities, which have recently attracted increasing attention from the research community. As an example, Contrastive Language-Image Pretraining (CLIP) [Radford et al., 2021] leverages a contrastive objective to obtain a modality-agnostic embedding space in which the paired images and texts are pulled closer and unpaired images and texts are pushed apart. Subsequently, CLIP can perform zero-shot visual prediction by matching the embeddings of test images and prompt-based textual descriptions (e.g., "a photo of a [CLASS]" and "this is a picture of a [CLASS]"), merely requiring the names of all the semantic classes in downstream tasks. Apart from the extensive research dedicated to the pre-training stage, numerous studies [Zhou et al., 2022b, Zhang et al., 2022b, Bahng et al., 2022] have concentrated on adapting VLMs to specific downstream tasks by using task-specific labeled data. This fine-tuning paradigm empowers VLMs to bridge both data and task gaps, leading to improved performance in recognition tasks. In addition to multi-class classification, these pioneering strategies have also been harnessed in a spectrum of computer vision tasks, including ordinal regression [Li et al., 2022b], point cloud understanding [Zhang et al., 2022a], and dense prediction [Rao et al., 2022]. When considering fine-tuning setups, most efforts have primarily revolved around fully supervised and few-shot supervised learning scenarios. To pursue annotation efficiency and scalability, several recent studies [Huang et al., 2022, Shu et al., 2022, Tanwisuth et al., 2023] have delved into the realm of unsupervised fine-tuning for VLMs, remarkably achieving performance on par with few-shot supervised

Deep neural networks (DNNs) often perform poorly in the presence of domain shift and category shift. How to upcycle DNNs and adapt them to the target task remains an important open problem. Unsupervised Domain Adaptation (UDA), especially recently proposed Source-free Domain Adaptation (SFDA), has become a promising technology to address this issue. Nevertheless, existing SFDA methods require that the source domain and target domain share the same label space, consequently being only applicable to the vanilla closed-set setting. In this paper, we take one step further and explore the Source-free Universal Domain Adaptation (SF-UniDA). The goal is to identify "known" data samples under both domain and category shift, and reject those "unknown" data samples (not present in source classes), with only the knowledge from standard pre-trained source model. To this end, we introduce an innovative global and local clustering learning technique (GLC). Specifically, we design a novel, adaptive one-vs-all global clustering algorithm to achieve the distinction across different target classes and introduce a local k-NN clustering strategy to alleviate negative transfer. We examine the superiority of our GLC on multiple benchmarks with different category shift scenarios, including partial-set, open-set, and open-partial-set DA. Remarkably, in the most challenging open-partial-set DA scenario, GLC outperforms UMAD by 14.8\% on the VisDA benchmark. The code is available at https://github.com/ispc-lab/GLC.

A typical multi-source domain adaptation (MSDA) approach aims to transfer knowledge learned from a set of labeled source domains, to an unlabeled target domain. Nevertheless, prior works strictly assume that each source domain shares the identical group of classes with the target domain, which could hardly be guaranteed as the target label space is not observable. In this paper, we consider a more versatile setting of MSDA, namely Generalized Multi-source Domain Adaptation, wherein the source domains are partially overlapped, and the target domain is allowed to contain novel categories that are not presented in any source domains. This new setting is more elusive than any existing domain adaptation protocols due to the coexistence of the domain and category shifts across the source and target domains. To address this issue, we propose a variational domain disentanglement (VDD) framework, which decomposes the domain representations and semantic features for each instance by encouraging dimension-wise independence. To identify the target samples of unknown classes, we leverage online pseudo labeling, which assigns the pseudo-labels to unlabeled target data based on the confidence scores. Quantitative and qualitative experiments conducted on two benchmark datasets demonstrate the validity of the proposed framework.