Search Results for author: Seokjun Seo

Found 8 papers, 6 papers with code

Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few Utterances

1 code implementation NAACL 2022 Seungju Han, Beomsu Kim, Jin Yong Yoo, Seokjun Seo, SangBum Kim, Enkhbayar Erdenee, Buru Chang

To better reflect the style of the character, PDP builds the prompts in the form of dialog that includes the character's utterances as dialog history.

Chatbot Retrieval

Understanding and Improving the Exemplar-based Generation for Open-domain Conversation

1 code implementation NLP4ConvAI (ACL) 2022 Seungju Han, Beomsu Kim, Seokjun Seo, Enkhbayar Erdenee, Buru Chang

Extensive experiments demonstrate that our proposed training method alleviates the drawbacks of the existing exemplar-based generative models and significantly improves the performance in terms of appropriateness and informativeness.

Informativeness Retrieval

Distilling the Knowledge of Large-scale Generative Models into Retrieval Models for Efficient Open-domain Conversation

1 code implementation Findings (EMNLP) 2021 Beomsu Kim, Seokjun Seo, Seungju Han, Enkhbayar Erdenee, Buru Chang

G2R consists of two distinct techniques of distillation: the data-level G2R augments the dialogue dataset with additional responses generated by the large-scale generative model, and the model-level G2R transfers the response quality score assessed by the generative model to the score of the retrieval model by the knowledge distillation loss.

Knowledge Distillation Retrieval

Disentangling Label Distribution for Long-tailed Visual Recognition

2 code implementations CVPR 2021 Youngkyu Hong, Seungju Han, Kwanghee Choi, Seokjun Seo, Beomsu Kim, Buru Chang

Although this method surpasses state-of-the-art methods on benchmark datasets, it can be further improved by directly disentangling the source label distribution from the model prediction in the training phase.

Image Classification Long-tail Learning

MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targets

no code implementations19 Nov 2019 Sungjoo Ha, Martin Kersner, Beomsu Kim, Seokjun Seo, Dongyoung Kim

When there is a mismatch between the target identity and the driver identity, face reenactment suffers severe degradation in the quality of the result, especially in a few-shot setting.

Disentanglement Face Reenactment

Temporal Convolution for Real-time Keyword Spotting on Mobile Devices

3 code implementations8 Apr 2019 Seungwoo Choi, Seokjun Seo, Beomjun Shin, Hyeongmin Byun, Martin Kersner, Beomsu Kim, Dongyoung Kim, Sungjoo Ha

In addition, we release the implementation of the proposed and the baseline models including an end-to-end pipeline for training models and evaluating them on mobile devices.

Ranked #14 on Keyword Spotting on Google Speech Commands (Google Speech Commands V2 12 metric)

Keyword Spotting

Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification

no code implementations16 Nov 2017 Sungmin Rhee, Seokjun Seo, Sun Kim

In this paper, we proposed a hybrid model, which integrates two key components 1) graph convolution neural network (graph CNN) and 2) relation network (RN).

Classification General Classification +3

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